{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/bradleywhitlock/CDP-Exploration/blob/master/Introduction_to_Data_Exploration_CDP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eAXnMj5E9jWG",
    "colab_type": "text"
   },
   "source": [
    "# Introduction to Data Visualization @ Cognite\n",
    "\n",
    "## What this notebook will achieve\n",
    "\n",
    "* Extract live data from an oil rig in the North Sea.\n",
    "\n",
    "* Visualize and inspect data directly from the Cognite Data Platform.\n",
    "\n",
    "* How to run a machine learning model on the data.\n",
    "\n",
    "\n",
    "## Getting started\n",
    "\n",
    "* Having a basic understanding of Python concepts will help to understand the process.\n",
    "\n",
    "* Cognite has released *live* data to the public on the Cognite Data Platform streaming from [Valhall](https://www.akerbp.com/en/our-assets/production/valhall/), one of Aker's oil fields.\n",
    "\n",
    "* To access the data, generate an API Key on [Open Industrial Data](https://openindustrialdata.com/). Get your key via the Google Access platform. You will be asked to fill out some personal information to generate your personal key.\n",
    "\n",
    "* Visualize some of the machines (assets) on Valhall with Cognite's [Operational Intelligence](https://opint.cogniteapp.com/publicdata/infographics/-LOHKEJPLvt0eRIZu8mE) dashboard. This data on this page shows is streaming live data from the Valhall oil field located in the North Sea.\n",
    "\n",
    "* To understand how to interact with the data using the Python SDK ([Docs](https://cognite-sdk-python.readthedocs-hosted.com/en/latest/)) follow along in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k8-Oj493ReNU",
    "colab_type": "text"
   },
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Lf5pnaDBLnc",
    "colab_type": "text"
   },
   "source": [
    "#### Install the Cognite SDK package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "pbgkP3p59iiK",
    "colab_type": "code",
    "outputId": "c8220cde-0930-414d-8d53-231b1052d15b",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting cognite-sdk\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/1a/a551329780eeab9f665ab655dd24c2e3b616efdfe86bf7f532b3c4895f87/cognite_sdk-0.12.13-py3-none-any.whl (54kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 2.5MB/s \n",
      "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from cognite-sdk) (2.18.4)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from cognite-sdk) (0.22.0)\n",
      "Requirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from cognite-sdk) (3.6.1)\n",
      "Collecting cognite-logger>=0.3 (from cognite-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/9a/28/12d35b2ea77df3d6525d60a9fa6b16b7547c1aab85a769b60d6d9c7dc4c3/cognite-logger-0.3.2.tar.gz\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->cognite-sdk) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->cognite-sdk) (2018.11.29)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->cognite-sdk) (2.6)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->cognite-sdk) (1.22)\n",
      "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas->cognite-sdk) (2.5.3)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from pandas->cognite-sdk) (1.14.6)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->cognite-sdk) (2018.9)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->cognite-sdk) (40.8.0)\n",
      "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from protobuf->cognite-sdk) (1.11.0)\n",
      "Collecting python-json-logger==0.1.8 (from cognite-logger>=0.3->cognite-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/00/32/8ca0071f8df6c4c7d0107f1891fc88ec7b7169000cc591cd30280235604d/python_json_logger-0.1.8-py2.py3-none-any.whl\n",
      "Building wheels for collected packages: cognite-logger\n",
      "  Building wheel for cognite-logger (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/e5/92/dd/a6992dd3087f76743c449a872bebfebca6b9c414dfef39f6ac\n",
      "Successfully built cognite-logger\n",
      "Installing collected packages: python-json-logger, cognite-logger, cognite-sdk\n",
      "Successfully installed cognite-logger-0.3.2 cognite-sdk-0.12.13 python-json-logger-0.1.8\n"
     ]
    }
   ],
   "source": [
    "!pip install cognite-sdk \"cognite-logger==0.3.*\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CN5J9FmPBRLk",
    "colab_type": "text"
   },
   "source": [
    "#### Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "uFkQ73a44wmU",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "from datetime import datetime, timedelta\n",
    "from datetime import datetime\n",
    "from getpass import getpass\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from cognite import CogniteClient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pr0CQECZBWW3",
    "colab_type": "text"
   },
   "source": [
    "#### Connect to the Cognite Data Platform\n",
    "* This client object is how all queries will be sent to the Cognite API to retrieve data.\n",
    "\n",
    "When prompted for your API key, use the key generated by open industrial data as mentioned in the Getting Started steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "E8CdoRFu9n2t",
    "colab_type": "code",
    "outputId": "43130697-9674-407a-cac4-c871a9c82757",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open Industrial Data API-KEY: ··········\n"
     ]
    }
   ],
   "source": [
    "client = CogniteClient(api_key=getpass(\"Open Industrial Data API-KEY: \"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VKFPBwYsRjHL",
    "colab_type": "text"
   },
   "source": [
    "## Accessing Cognite Data Platform (CDP)\n",
    "* The CDP organizes digital information about the physical world.\n",
    "* There are 6 kinds of objects stored on the CDP. Each of these objects in the CDP are labelled with a unique ID. Information regarding a specific Asset, Event, etc are often retrieved using this ID.\n",
    "\n",
    "  * [Assets](https://doc.cognitedata.com/api/0.5/#tag/Assets) are digital representations of physical objects or groups of objects, and assets are organized into an asset hierarchy. For example, an asset can represent a water pump which is part of a subsystem on an oil platform.\n",
    "  \n",
    "  * [Event](https://doc.cognitedata.com/api/0.5/#tag/Events) objects store complex information about multiple assets over a time period. For example, an event can describe two hours of maintenance on a water pump and some associated pipes.\n",
    "  \n",
    "  * A [File](https://doc.cognitedata.com/api/0.5/#tag/Files) stores a sequence of bytes connected to one or more assets. For example, a file can contain a piping and instrumentation diagram (P&IDs) showing how multiple assets are connected.\n",
    "  \n",
    "  * A [Time Series](https://doc.cognitedata.com/api/0.5/#tag/Time-series) consists of a sequence of data points connected to a single asset. For example: A water pump asset can have a temperature time series that records a data point in units of °C every second.\n",
    "  \n",
    "  * [Sequence](https://doc.cognitedata.com/api/0.5/#tag/Sequences) are similar to time series in that they are a key value pair, but rather than using a timestamp as the key, another measurment such as depth could be the key. For example, this is used in practice when drilling and taking measurments at various depths.\n",
    "  \n",
    "  * A [3D](https://doc.cognitedata.com/api/0.5/#tag/3D) model is typically built up by a hierarchical structure. This looks very similar to how we organize our internal asset hierarchy. 3D models are visualized via Cognite's dashboards.\n",
    "  \n",
    "* It is important to refer back to the [SDK](https://cognite-sdk-python.readthedocs-hosted.com/en/latest/cognite.html) for specific details on arguments on all avaiable methods on how to access these objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fn9FD2lMWUwI",
    "colab_type": "text"
   },
   "source": [
    "### Collecting Asset Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dRiusGapFmT8",
    "colab_type": "text"
   },
   "source": [
    "#### Retrieve a list of all Assets\n",
    "\n",
    "* There are thousands of Assets in the CDP, we can have a look at a few examples.\n",
    "\n",
    "* This will generate a list of assets from the CDP with no particular filters, this is a random result. Generally we would want to apply filters when retrieving records.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "_sACMOGWFmna",
    "colab_type": "code",
    "outputId": "41e482b7-70b4-41f7-bfc9-014e77205a5c",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374.0
    }
   },
   "outputs": [
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       "2  {'ELC_STATUS_ID': '1211', 'SOURCE_DB': 'workma...   \n",
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       "2         23-1ST STAGE COMP ENCLOSURE-PH  4856008121737468   \n",
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       "\n",
       "                                                path  \n",
       "0  [6687602007296940, 4650652196144007, 311145472...  \n",
       "1  [6687602007296940, 4650652196144007, 311145472...  \n",
       "2  [6687602007296940, 4650652196144007, 311145472...  \n",
       "3  [6687602007296940, 4650652196144007, 311145472...  \n",
       "4  [6687602007296940, 4650652196144007, 311145472...  "
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.assets.get_assets().to_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ysq_kFWLGYFV",
    "colab_type": "text"
   },
   "source": [
    "#### Decide on which asset we want to explore\n",
    "* To get started exploring data in the CDP, we must first decide on which Asset we want to gather information from.\n",
    "\n",
    "* Some asset names may be retrieved from the [Op Int](https://opint.cogniteapp.com/publicdata/infographics/-LOHKEJPLvt0eRIZu8mE) dashboard.\n",
    "\n",
    "* Here is a screehshot of the [OpInt Dashboard](https://drive.google.com/open?id=1f_7nJaJu5Xgr3Oq09mIZ0KwjBAYbzEUQ) incase the page does not load.\n",
    "\n",
    "* Some example asset names are:\n",
    "  * 23-HA-9103\n",
    "  * 23-PV-92583\n",
    "  * 23-VG-9101\n",
    "  \n",
    "The *fuzzy* search for an asset can be performed as followed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "L9DHHUpwBJOv",
    "colab_type": "code",
    "outputId": "6917abc2-a7ad-4432-ece4-d4fe42d3dc56",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289.0
    }
   },
   "outputs": [
    {
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       "      <td>5345363121325760</td>\n",
       "      <td>0</td>\n",
       "      <td>{'ELC_STATUS_ID': '1211', 'RES_ID': '531303', ...</td>\n",
       "      <td>23-HA-9114</td>\n",
       "      <td>2513266419866445</td>\n",
       "      <td>[6687602007296940, 4650652196144007, 311145472...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>VRD - 1ST STAGE COMPRESSOR LUBE OIL COOLER B</td>\n",
       "      <td>6838563873305104</td>\n",
       "      <td>0</td>\n",
       "      <td>{'ELC_STATUS_ID': '1211', 'RES_ID': '786896', ...</td>\n",
       "      <td>23-HA-9107B</td>\n",
       "      <td>2137557577165478</td>\n",
       "      <td>[6687602007296940, 4650652196144007, 311145472...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   createdTime  depth                                   description  \\\n",
       "0            0      6                VRD - 1ST STAGE SUCTION COOLER   \n",
       "1            0      6            VRD - 1ST STAGE DISCHARGE COOLER 2   \n",
       "2            0      8  VRD - 1ST STAGE COMPRESSOR LUBE OIL COOLER A   \n",
       "3            0      6            VRD - 1ST STAGE DISCHARGE COOLER 1   \n",
       "4            0      8  VRD - 1ST STAGE COMPRESSOR LUBE OIL COOLER B   \n",
       "\n",
       "                 id  lastUpdatedTime  \\\n",
       "0  2861239574637735                0   \n",
       "1  1305599545806638                0   \n",
       "2  4965752723543746                0   \n",
       "3  5345363121325760                0   \n",
       "4  6838563873305104                0   \n",
       "\n",
       "                                            metadata         name  \\\n",
       "0  {'ELC_STATUS_ID': '1211', 'RES_ID': '531306', ...   23-HA-9103   \n",
       "1  {'ELC_STATUS_ID': '1211', 'RES_ID': '531303', ...   23-HA-9115   \n",
       "2  {'ELC_STATUS_ID': '1211', 'RES_ID': '786890', ...  23-HA-9107A   \n",
       "3  {'ELC_STATUS_ID': '1211', 'RES_ID': '531303', ...   23-HA-9114   \n",
       "4  {'ELC_STATUS_ID': '1211', 'RES_ID': '786896', ...  23-HA-9107B   \n",
       "\n",
       "           parentId                                               path  \n",
       "0  2513266419866445  [6687602007296940, 4650652196144007, 311145472...  \n",
       "1  2513266419866445  [6687602007296940, 4650652196144007, 311145472...  \n",
       "2  2137557577165478  [6687602007296940, 4650652196144007, 311145472...  \n",
       "3  2513266419866445  [6687602007296940, 4650652196144007, 311145472...  \n",
       "4  2137557577165478  [6687602007296940, 4650652196144007, 311145472...  "
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_name = \"23-HA-9103\"\n",
    "asset_df = client.assets.search_for_assets(name=asset_name).to_pandas()\n",
    "asset_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "THRHdIXwS-bf",
    "colab_type": "text"
   },
   "source": [
    "#### Get information on the asset of interest\n",
    "\n",
    "* We can filter the assets to get asset-specific details based on asset_name\n",
    "\n",
    "* The *get_asset()* interface provides the same information for 1 specific asset based on the provided ID\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "kxnrtYoYSBGo",
    "colab_type": "code",
    "outputId": "f1d57fa0-30e0-4f21-ff1b-1f7c5d9d6c32",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328.0
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "                                                                 0\n",
       "id                                                2861239574637735\n",
       "depth                                                            6\n",
       "name                                                    23-HA-9103\n",
       "parentId                                          2513266419866445\n",
       "description                         VRD - 1ST STAGE SUCTION COOLER\n",
       "metadata         {'ELC_STATUS_ID': '1211', 'RES_ID': '531306', ...\n",
       "createdTime                                                      0\n",
       "lastUpdatedTime                                                  0\n",
       "path             [6687602007296940, 4650652196144007, 311145472..."
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_id = asset_df[asset_df[\"name\"] == asset_name].iloc[0]['id']\n",
    "asset = client.assets.get_asset(asset_id=asset_id).to_pandas()\n",
    "asset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X75GOITO_m1q",
    "colab_type": "text"
   },
   "source": [
    "#### How do we get Asset relationships?\n",
    "\n",
    "* The interface *get_asset_subtree()* can be used to retrieve the *children* of an Asset. \n",
    "\n",
    "* Each Asset is given various properties, some of the useful ones for this method are:\n",
    "\n",
    "  * Depth: The number of edges from the parent node\n",
    "  \n",
    "  * Description: Includes information such as the platform and type of sensor being monitored\n",
    "  \n",
    "We will generate a list of all children of the main asset of interest. This is done by specifying a depth of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "1OPecRPv6xsU",
    "colab_type": "code",
    "outputId": "aa33544c-6a3c-4cb8-f3cc-3933bb116289",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289.0
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "   createdTime  depth                           description                id  \\\n",
       "0            0      6        VRD - 1ST STAGE SUCTION COOLER  2861239574637735   \n",
       "1            0      7   VRD - PH 1STSTGSUCTCLR GAS IN EQUAL  1659945743622682   \n",
       "2            0      7      VRD - PH 1ST STG SUCT COOL SHELL  1850550479549882   \n",
       "3            0      7  VRD - PH 1STSTGSUCTCOOL SHELL PSV IN   274450897701725   \n",
       "4            0      7        VRD - PH 1STSTGSUCTCOOL GAS IN  2814662602621825   \n",
       "\n",
       "   lastUpdatedTime                                           metadata  \\\n",
       "0                0  {'ELC_STATUS_ID': '1211', 'RES_ID': '531306', ...   \n",
       "1                0  {'ELC_STATUS_ID': '1211', 'SOURCE_DB': 'workma...   \n",
       "2                0  {'ELC_STATUS_ID': '1211', 'RES_ID': '551664', ...   \n",
       "3                0  {'ELC_STATUS_ID': '1225', 'RES_ID': '444134', ...   \n",
       "4                0  {'ELC_STATUS_ID': '1211', 'RES_ID': '495056', ...   \n",
       "\n",
       "             name          parentId  \\\n",
       "0      23-HA-9103  2513266419866445   \n",
       "1  23-ESDV-92501B  2861239574637735   \n",
       "2    45-PSV-92514  2861239574637735   \n",
       "3  45-HV-92510-01  2861239574637735   \n",
       "4     23-PT-92512  2861239574637735   \n",
       "\n",
       "                                                path  \n",
       "0  [6687602007296940, 4650652196144007, 311145472...  \n",
       "1  [6687602007296940, 4650652196144007, 311145472...  \n",
       "2  [6687602007296940, 4650652196144007, 311145472...  \n",
       "3  [6687602007296940, 4650652196144007, 311145472...  \n",
       "4  [6687602007296940, 4650652196144007, 311145472...  "
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtree_df = client.assets.get_asset_subtree(asset_id=asset_id, depth=1).to_pandas()\n",
    "subtree_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KeCoOb_AWPv_",
    "colab_type": "text"
   },
   "source": [
    "### Collecting Time Series Information and Data Points\n",
    "\n",
    "* Time Series objects contain the bulk of information in the CDP.\n",
    "\n",
    "* Time Series objects are generally linked to an asset through the asset_id field.\n",
    "\n",
    "* The *get_time_series()* method has a variety of filters, all are listed in the SDK.\n",
    "\n",
    "* By specifying **asset_id** we will get a dataframe of all the associated time series objects with that asset, this is seen below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oGg2xxBQUQUa",
    "colab_type": "text"
   },
   "source": [
    "#### Compile a list of time series objects under the asset\n",
    "* For each of the children assets, get the associated time series objects and merge them into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "2n-VPjJf6xum",
    "colab_type": "code",
    "outputId": "428edc8f-5501-4207-fcb0-21f693dc1c04",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421.0
    }
   },
   "outputs": [
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       "      <td>7835659687560027</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Shell</td>\n",
       "      <td>1804879717768950</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>VAL_45-PDT-92506:X.Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>786220428505816</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Gas In</td>\n",
       "      <td>3518012501014915</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>VAL_23-FT-92512:X.Value</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             assetId  createdTime                    description  \\\n",
       "0   2814662602621825            0       PH 1stStgSuctCool Gas In   \n",
       "1   4840206559741735            0       PH 1stStgSuctCool Gas In   \n",
       "2   5193727763959013            0      PH 1stStgSuctCool Gas Out   \n",
       "3   5439867226448359            0       PH 1stStgSuctCool Gas In   \n",
       "4   5497674254221099            0   PH 1stStgSuctCool CoolMed In   \n",
       "5   5552927149248373            0  PH 1stStgSuctCool Gas In ESDV   \n",
       "6   5891006566061532            0   PH 1stStgSuctClr CoolMed Out   \n",
       "7   6681756879590530            0        PH 1stStgSuctCool Tubes   \n",
       "8    705952550422793            0  PH 1stStgSuctCool CoolMed Out   \n",
       "9   7390359759479147            0  PH 1stStgSuctClr CoolMed Sply   \n",
       "10  7835659687560027            0        PH 1stStgSuctCool Shell   \n",
       "11   786220428505816            0       PH 1stStgSuctCool Gas In   \n",
       "\n",
       "                  id  isStep  isString  lastUpdatedTime  \\\n",
       "0   6156871056679530   False     False                0   \n",
       "1   5474031062875475   False     False                0   \n",
       "2   7491681405625794   False     False                0   \n",
       "3    988967451935968   False     False                0   \n",
       "4   1920137775628302   False     False                0   \n",
       "5   5880632484472759   False     False                0   \n",
       "6   3929156348065703   False     False                0   \n",
       "7   7891376783606949   False     False                0   \n",
       "8   3529821833330815   False     False                0   \n",
       "9   2288456938237513   False     False                0   \n",
       "10  1804879717768950   False     False                0   \n",
       "11  3518012501014915   False     False                0   \n",
       "\n",
       "                        name  \n",
       "0    VAL_23-PT-92512:X.Value  \n",
       "1    VAL_23-TT-92502:X.Value  \n",
       "2    VAL_23-PT-92504:X.Value  \n",
       "3    VAL_23-TT-92512:X.Value  \n",
       "4    VAL_45-FT-92506:X.Value  \n",
       "5   VAL_23-PDT-92501:X.Value  \n",
       "6    VAL_45-TT-92508:X.Value  \n",
       "7   VAL_23-PDT-92502:X.Value  \n",
       "8    VAL_45-PT-92508:X.Value  \n",
       "9    VAL_45-TT-92506:X.Value  \n",
       "10  VAL_45-PDT-92506:X.Value  \n",
       "11   VAL_23-FT-92512:X.Value  "
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ts_df = pd.DataFrame()\n",
    "\n",
    "for index, asset in subtree_df.iterrows():\n",
    "  ts_df = client.time_series.get_time_series(asset_id=int(asset['id'])).to_pandas()\n",
    "  \n",
    "  if not ts_df.empty:\n",
    "    all_ts_df = all_ts_df.append(ts_df, ignore_index=True)\n",
    "    \n",
    "all_ts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YIejfGjYWwNY",
    "colab_type": "text"
   },
   "source": [
    "#### View datapoints for one of the time series\n",
    "\n",
    "* Now that we have a list of all of the time series directly related to the immediete childern of our asset, we can retrieve datapoints for some of the them.\n",
    "\n",
    "* A Datapoint in the CDP is stored as a key value pair\n",
    "\n",
    "  * timestamp is the time since epoch in milliseconds\n",
    "  \n",
    "  * value is the reading from the sensor\n",
    "  \n",
    "The identifier to retrieve Datapoints is the **name** column from the DataFrame above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "d4wYaEyJWtrq",
    "colab_type": "code",
    "outputId": "665ed30e-17b3-4d24-d7dc-59d680dfe00d",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204.0
    }
   },
   "outputs": [
    {
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       "       timestamp     value\n",
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     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.datapoints.get_datapoints(\"VAL_45-PT-92508:X.Value\", start=\"10d-ago\").to_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S3fBvnnKL3ii",
    "colab_type": "text"
   },
   "source": [
    "## Practical use case of the data in the CDP\n",
    "\n",
    "* When gathering data from oil platforms, specific sensors can be very expensive to install and maintain.\n",
    "\n",
    "* A technique often used it to use a combination of other sensor readings to predict another.\n",
    "\n",
    "* The rest of the notebook will be used to retrieve data and create the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mLJBe0WuWKDq",
    "colab_type": "text"
   },
   "source": [
    "#### Collect datapoints from CDP\n",
    "\n",
    "* The following 4 columns were chosen in an effort to estimate the output pressure of the suction cooler\n",
    "\n",
    "* Input variables:\n",
    "\n",
    "  * FT: Input Flow Rate\n",
    "  \n",
    "  * PT: Input Pressure\n",
    "  \n",
    "  * TT: Input Temperature\n",
    "  \n",
    "* Output variables:\n",
    "\n",
    "  * Output *Pressure* \n",
    "  \n",
    "The time series names are defined in the *in_ts_names* and *out_ts_names* lists below.\n",
    "\n",
    "\n",
    "#### Aside: Cool exercise when the introduction is completed!!!\n",
    "\n",
    "* Add/Delete input time series or change the output and see how the predictions and results change!\n",
    "\n",
    "  * This can be done \n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "l4-C138i-HBy",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "in_ts_names = [\"VAL_23-FT-92512:X.Value\", \"VAL_23-PT-92512:X.Value\", \"VAL_23-TT-92502:X.Value\"]\n",
    "out_ts_name = \"VAL_23-PT-92504:X.Value\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lvr3qJp_OQgF",
    "colab_type": "text"
   },
   "source": [
    "#### Retrieve Data Points from CDP\n",
    "\n",
    "* Datapoints are returned in a pandas dataframe using the method *get_datapoints_frame()*, [documentation](https://cognite-sdk-python.readthedocs-hosted.com/en/latest/cognite.html#cognite.client.stable.datapoints.DatapointsClient.get_datapoints_frame).\n",
    "\n",
    "* The advantage of using this method over *get_datapoints()* is that the data is aggregated to a common timestamp. The processing is taken care of in the API.\n",
    "\n",
    "  * The common timestep is assigned using *granularity* argument. In this case, 1 minute was chosen.\n",
    " \n",
    "Also performed is a step to rename the columns to their original, as the get_datapoints_frame method adds suffixes based on the aggregate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "I1qoX1WY-HEV",
    "colab_type": "code",
    "outputId": "4ee6e78f-4ff4-44e9-e8ec-b7c7e343513b",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204.0
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>44.232420</td>\n",
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       "       timestamp  VAL_23-FT-92512:X.Value  VAL_23-PT-92512:X.Value  \\\n",
       "0  1533081600000             91120.918480                 3.374376   \n",
       "1  1533081660000             86259.975220                 3.395966   \n",
       "2  1533081720000             90750.665657                 3.410101   \n",
       "3  1533081780000             91948.525170                 3.358822   \n",
       "4  1533081840000             93779.111997                 3.429458   \n",
       "\n",
       "   VAL_23-TT-92502:X.Value  VAL_23-PT-92504:X.Value  \n",
       "0                44.184863                 3.251437  \n",
       "1                44.232420                 3.274299  \n",
       "2                44.161438                 3.287578  \n",
       "3                43.986025                 3.237081  \n",
       "4                43.797913                 3.306391  "
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_names = in_ts_names + [out_ts_name]\n",
    "\n",
    "train_start_date = datetime(2018, 8, 1)\n",
    "# 30 days of training data chosen arbitrarily\n",
    "train_end_date = train_start_date + timedelta(days=30)\n",
    "\n",
    "datapoints_df = client.datapoints.get_datapoints_frame(time_series=ts_names,\n",
    "                                                       aggregates=['avg'],\n",
    "                                                       granularity='1m',\n",
    "                                                       start=train_start_date,\n",
    "                                                       end=train_end_date\n",
    "                                                       )\n",
    "# Remove the \"|average\" appended to the end of the column names\n",
    "datapoints_df.columns = [\"timestamp\"] + ts_names\n",
    "datapoints_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cg63RY4MP1Sv",
    "colab_type": "text"
   },
   "source": [
    "#### Visualize the Time Series data\n",
    "* Utilize matplotlib to visualize each of the time series plots\n",
    "\n",
    "The bottom right plot is the output time series, while the other 3 are the inputs used to create an estimate for the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "m3F0ZbBBDDB6",
    "colab_type": "code",
    "outputId": "4767616c-90b9-4e74-aef1-c1e49f034082",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 879.0
    }
   },
   "outputs": [
    {
     "data": {
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AJxLCfl11AIAt+xL8XBIRERER8QcFfCIBSKsSi4iIiEhVUMAnEkC036iIiIiI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\niRm89/V69h5JLpL3wNFUHnx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Ht92BFPhl2T6+nr+LNx/pSevmDTlSqLUz/++M7DzSMnP5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jXpschY5xanhcTTzZ/60R259Zp0ZJmqPVdrN+dWikGSbY9JcrmD+AA4oIDWb23Oqve0N/PTJOmt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\n/wPaRq38Q+9MXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = list(datapoints_df.columns)\n",
    "cols.remove('timestamp')\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,15))\n",
    "for i, col in enumerate(cols):\n",
    "  datapoints_df.plot(x='timestamp', y=col, ax=axes[int(i>1), i%2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-TPMQNJmQbEh",
    "colab_type": "text"
   },
   "source": [
    "# Construct a model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GxVhJGMTQwv-",
    "colab_type": "text"
   },
   "source": [
    "### Gather the training data using the data from the figures above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "dcAhxWwVN08Q",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "train_X = datapoints_df[in_ts_names].values\n",
    "train_y = datapoints_df[out_ts_name].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TTJ7oms6NK8n",
    "colab_type": "text"
   },
   "source": [
    "### Get a seperate DataFrame from CDP for predictions\n",
    "\n",
    "* The data which we will use to predict the output pressure will be stored in a seperate dataframe as collected below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "B6md7J82NKTI",
    "colab_type": "code",
    "outputId": "5ac04cf4-e1e8-40bf-f5f5-2e949baab9ee",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>VAL_23-FT-92512:X.Value</th>\n",
       "      <th>VAL_23-PT-92512:X.Value</th>\n",
       "      <th>VAL_23-TT-92502:X.Value</th>\n",
       "      <th>VAL_23-PT-92504:X.Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1535673600000</td>\n",
       "      <td>96596.397652</td>\n",
       "      <td>3.390761</td>\n",
       "      <td>44.822433</td>\n",
       "      <td>3.266866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1535673660000</td>\n",
       "      <td>92941.748459</td>\n",
       "      <td>3.382212</td>\n",
       "      <td>44.879119</td>\n",
       "      <td>3.259393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1535673720000</td>\n",
       "      <td>100634.003031</td>\n",
       "      <td>3.477319</td>\n",
       "      <td>45.053927</td>\n",
       "      <td>3.348573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1535673780000</td>\n",
       "      <td>103948.895082</td>\n",
       "      <td>3.434556</td>\n",
       "      <td>45.404786</td>\n",
       "      <td>3.305596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1535673840000</td>\n",
       "      <td>101079.342794</td>\n",
       "      <td>3.348590</td>\n",
       "      <td>45.698323</td>\n",
       "      <td>3.222974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp  VAL_23-FT-92512:X.Value  VAL_23-PT-92512:X.Value  \\\n",
       "0  1535673600000             96596.397652                 3.390761   \n",
       "1  1535673660000             92941.748459                 3.382212   \n",
       "2  1535673720000            100634.003031                 3.477319   \n",
       "3  1535673780000            103948.895082                 3.434556   \n",
       "4  1535673840000            101079.342794                 3.348590   \n",
       "\n",
       "   VAL_23-TT-92502:X.Value  VAL_23-PT-92504:X.Value  \n",
       "0                44.822433                 3.266866  \n",
       "1                44.879119                 3.259393  \n",
       "2                45.053927                 3.348573  \n",
       "3                45.404786                 3.305596  \n",
       "4                45.698323                 3.222974  "
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_start_date = train_end_date\n",
    "# Make the prediction on 1 hour of data\n",
    "predict_end_date = train_end_date + timedelta(hours=1)\n",
    "\n",
    "predict_df = client.datapoints.get_datapoints_frame(time_series=ts_names,\n",
    "                                                       aggregates=['avg'],\n",
    "                                                       granularity='1m',\n",
    "                                                       start=predict_start_date,\n",
    "                                                       end=predict_end_date\n",
    "                                                       )\n",
    "predict_df.columns = [\"timestamp\"] + ts_names\n",
    "predict_df = predict_df.fillna(method='ffill')\n",
    "predict_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DGxBny7gNXFZ",
    "colab_type": "text"
   },
   "source": [
    "## Linear Regression Model\n",
    "\n",
    "* As a simple starting point we will check to see how a linear regression model performs to predict the output pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vho0ucbfR1ms",
    "colab_type": "text"
   },
   "source": [
    "### Utilize *sklearn*  to create a basic linear regression model\n",
    "\n",
    "* Sklearn is common package utilized to import and deploy data science models. Linear Regression is only 1 of many options for constructing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "bDsh5JRvE4jc",
    "colab_type": "code",
    "outputId": "47273fcc-3f93-485a-8960-2673de1558a1",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 660.0
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  series.name = label\n"
     ]
    },
    {
     "data": {
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JJ+KDHzwb3/7217Fw4QDy+bXYsWM7vva1S5DLHdjJZZrXUHFFEAQRIKqp1fw/0Z1s3LgB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VvHFbWKtep5/K2P/HXoso7/863fwhuP3mLfN+O0oONRMWqeR59eWkR35c2PXF0EMLyGJg4+88Vb\nGFgufuGHnkGzGv+1XlmvQ1UkPOqxn3e5cVEcJ7LqJ173ZQSBdoWoJ86ltep5ECpDqnWwtlxOuw0Y\nVq6qpSVX7PxwFnh+ZMX3DLkCgL/84zdQ1RX8zhffP7HQE4goV0p+ypWhK6DgF+58MlcAcL6yCtuz\nxQ6+KO7utMEpV7vHfs9xk4JAaIESKb9pQSCsYxh4g6FerTjg5OrYYsrQ8pByFRDguIF2Jwy0Z1Gu\njswjLOktXFljTfPNkSb5smLjfA1SjaluReetAPZ+a1Jgy81BuQIAVWNHe2gLsocXm9qo6spMQhkt\nEuW2IO9C2mxs4Fc+9B+DEIL/8cv/DLeP74qn6dS2oOuLUfM0a6zyArPEKGzfjv2wkhWXU0wM3tvt\n4I/feIiL56r4sZeTHcOKLOGZCw30pQNIRMLF6ro4TtIUVOpBxk8hJ//2EpFABk1IlS6kEvczDdmC\nJSVXnsPO2TNb8JSgWdPwS596FgPLw7/8/LsnPs6D5xU1P+WqGmlpz0u5AoC16ioA4PFg/8TH7kRy\nZcddRhC7YwpEgXAVB/IOtNc0gHhwqZNaueo6XTSqqsiCVVMoV6blgQpbMJ1y5fou2nYXy8YSfvhD\nF/HBq8u42GIrcMpuDV48V4VUY8dD0eSKrY/yoUvzy1wBgKKyG9loFYMLJ1aPEa9q6FsuBoEtGA36\nfmD5Wfz1F/8KHN/FP/3Wr4nhgKyBdmCxT+aGJovzaV7B5rXqeaiSGjvUTinFb/7Ru6AAfvnTH4CS\nQl2/vtEEqXSwrJ6DKqviOJFTjPnzaUEZJ48r2/FgdxoAoXjYy2fNTxGoV1QgmBYsa0O76wa24Bm5\nOj34kY9cwvVLTbz+9h7evD28RJRXJlRUbivkuAInsBwUScnU7MzBszOP+ydzV7cfRshVYAuOW30D\nRG4QUr7kqlnXUvv6XBUa+D2hWgGBLRjkHWIH2h0PcLMpV0dWGxQUS/oSnr+6jP/ql78PK1WmYI1T\nDssEVZFRXWbEerNe8KSgM1yJMJhDzxXA7B0gnN7iqqYHZ2aYHQhLRvumI2zB0ZvOh86/iF/56H+E\nvjvAV3a+zn5e2ioGzwdoYAsuWLkS9tCcgs0SkXCpfgGPeruxVImvfncP79w/wkdurOKlZ9LlBdfW\nKIjsQXPZ8mhVYjZwmg4lXWW2oDSGXO0dDuD32HWBV5+UEbWSK1eUUniCXJ3ZgqcGEiH4a//eTRAC\n/MYfbom9U0BoC1Y0dnHOJdAeKRI1XTMX1QoIydXemFD77YfHIIQpde1+QK741N7IySQRCQQEkLzc\nM1fhCZxOuaKyhZVG+H6lqmKw3Yhyle5mfxjUMKwYS+FrVPmannKTKwAg1Taoq0DxGoX+HH7+GIEK\nYs0pcyUH9g63alRJgUxkQHJmhtmByAocyxVTbOOe6D/1zCfwF2/8tPjv9IH2iC24SOVKlxeiYFyu\nX4JHPez09qZ+nuV4+O0vvAdFJvgP/vyN1D9PbTB12e6w65BPKeDLqcgVV64kevK42jnow++zc+x+\nxhb6IsFtQUKl1MdwkXDc8OHjTLk6Zbiy3sCnX9nE3uEAv/+nd8W/80B7LVCu8niqDJUrF313kHlS\nkOP8BFuQUoo7j9go8GrLwHHXBqUUXXf8GhpCCGSiBFUMeZMrRuySPh3V1RojfIqN5WZIoqK2YEWJ\nOy0YzVylswV5geiy0RL/1tR55qrctuDAHcCW2/B7TTzcL3ZdDz9/KjLPXM2HXElKsHA9csPTZZ31\nGMVYQTKcuRpAlzXI0vgpsk9f+VH8+8/8BJb1JXEOJoXr+SLQvkhyVYkoV3nkQOMi7hqcf/eVezho\nW/jJj17BWobeqH2Hkbj9HZ0NFgXXBColJ1eqSkAkH2QMudo97IMOGpAglVu5MlRAcSD5euEDLmlg\nl+ThIyu+J8kVAPzCDz+D5YaOP3jtrijaNG0PikxgqPkpV2HmyguUq3wuYi2tCVVS8WTEFnxybKJv\nuthcq6NV0+D5FD3TDZWrMfK/TJTcM1e1igpJSyc9S0SCIVVAVEvUMABsVUlS5cqyPQAEuqynLhE9\nCmoYlvVQueLWZdltQd6F5vdb2H5cLLnitmBVY8R3XoF2iSsQfkiIVEkDZDe5cuUOZlpkP/3MT+C/\n/8G/e6LWJC6imavF2oKRzNU8lavGbHK1f2zi3752F626hp/+xNVMP4//nMFxFY+e9DCwXNCUJa6S\nzFRSQk+S792DAUAlnDfW8LD7KNOi+CKhKhIj1V75VCuAZdf4w8dZoP0UoqIr+OU//wG4HsVv/OFW\nsC7Gg6EpIoeUi3IV2IJd04Tju7EVl1mYVMfA+6021+qia+q4Z6M3QbkCABlM6s6TXEmEoFJlF6I0\nNyGNVEBU+2TmSk5WIsqtqqpipL7Zh8pVxBYMrMuyB9p5+Jr2moWTKz4QUgvU2cGcbEFu70TVBJVo\nILIbK3PFP2dgMluw6HA3swUXb3sYmhzJXM2PXF2qXQABmTox+NtfeA+26+OXfvTZxMuVR7HdfYQK\nqQOuhve2j9lx6qUjV9yChneSXO0c9iERgmtLG3B8F7v9x5led1HwqQ8iO2JzRdlwZgs+BXjl5nn8\nuevn8NadQ7z+9h4GlgtDk6EG0yR5BNr5haFrMnKTF7kCmDVoehY6TniDD8lVQ3RNtbtW2JQ+RkWS\nuC2YY+YKAHSDXYjSXLhl3wBRXDRq4YU1WiKaZP2NpkqoKBVRhZEUB0HmalkPbUFuXZY9c8XtCWK2\nsP2kWCJocuVK16FK6tyUKwjlKjxWFKiA7MZab8LJVc+yYXpm4USD2YKLtz2GAu1ztAUNRcf56jls\ndx+N7enbuneIr353D9cvNfGJly5k+lkdu4sj6xiXgn6t97aPYdoeqC/DI+7Ynz8NJFCuqH/yuNo7\n6GN1yRD7O8tqDQ5cEyCAZ2cjrUXBcrwzW/C0gxCCv/KTz0FVJPyLz7+LnukMK1c5Bto7fGlzjuRq\nrRLkrvph7iqqXLWiyhWfFhxjeUiQc58WBADVCPqAkOJ3doIlvJXw5FJkSVhAeszSQ8v2YKgyKoFy\nlWbdyKF1BF3WUIncdCUioa7VSm8L3uuwZvb12nk8fNJnYd6CIEp4NRmGosOc07QgX4Dsu+HxK0MF\nIYAe49DjtmAneAAqulgxagvyFvlFYDjQPt+pscv1S+i7A6EKc/g+q14AWPWClDETxJdE31jeREWX\n8d52m02F+zIAmtx2CnJadES56psO2n0H68tVMZV7v1tOcsU7Dz1HYcdiycAXmwNntuCpxtpSBT/z\nyWto92zYjg9Dl/PdLcifim1OrvJ7QhR1DJGJwft7HTRrGpbqGppBkScjVz1osja2BkKiMkC8VB0y\n08AzV76TXH52bTX4HsM3H0nxAF+BROK9VtNm+8MqigEKmipkfWQeY1lfOhH+bGqNUgfaB+4Ae/0n\n2Gxcxub5OizHw/5xcYTH4tOCmoyKbMCaU6Cd2zvRGx6hweSgOvvmUQ1C711nuEC0KLien8vWgKyo\nLEi5AtgaGuBkmegXv/UQ9/e6+MGXLuDZS61xX5oIPG91uXkJ1y+1sHvQx+OjgRhySUpu+bHGG8Q5\ndg/Z33F9pYKN+sWZtuciwVc8UVdFd1A+ZciOkKtFluxmxfc8uQKAn/rYFVw8x57cDE0WJXN5rKao\nBItBuXKUry3IyRVTrgaWi8dHJp651AQhBK16qFx1nf7E7JMEGUT2ocj5To6Q4KnYTSE/24Pg5uP3\nR76nOzbvMAmW40HXZEFq+3aym5nlsbxaNG/F0dQaMD1L7JorG3iY/UpjQ+wYLDJ3JciVKkNX9Lll\nrmiwzNuLKFckuHkq2uxQcUVnx1NvTuTKcX0RJk6zwDgvLCrQDkTX4IQEpGc6+J0v3oKuyfjFTz2b\ny8950Hkkfh7fM/jm7QNhOyUlV9ymih5rAMRQ1IWVKgxFx1p1Ffc7Dxe6mHsS+BYCuBp6ZSRXjneW\nuXpaoCoS/upP3gQALNX0SKA9n/U3ADAInuLzVa64LciUqwePmYpy7SK7iAhbsMuUq0lTe3zyRVby\ntYx8yQL1Cfr92Z8bBaUU/W5gp44qQ7IL6iqxshJ8SGGIXCW8mR2OyVtxlD3UzsPsVxob2Fhlr7XI\n3JVYH6XJMGQdtmfP5ebiwQH1JThO5JgI8ldxVpzIkgRdk4WKVHig3fNFmHiRypWqSJAWEGgHohOD\n4Y7BP/raA3QHDn72k9ewVM9nHc929yE0WcNq5ZwgV2/dPRQPaEmjH/zzPWf41rkTkCu+sHmzsQHT\nM7E/OMz0+osAf4goq3I13AN3Rq5OPZ6/uoy/+1dfwS9+6tlQucqBXFV1/oSUf+aqpTehSoqwBXne\n6plLwf67gFwd9XqwfWeiciXGinPeh+USC3A1tHvJ3seB5cEx2c1nlFxRyQX15FhZAdv1QWlgUwXv\nO7+wxAXPhCxNUK7Yayxn7ur28T0AwNXmlbkoVzzQbmiK2O83j64rH0zN5FUQAOC77Jjm1R2zUNUV\nkRGbh3JFA2UtbT1IHiCEQFJdwJdy2RqRBC2tibpaG7LObgdru37kw5dy+RmO52Cnv4fL9YuQiITr\nl5ogCJa5C+Uq2fHJ7wl8PQuHsAWDhc28y6uMuat+6W3BaBVD+V5fXJyRqwhubLTQqmmitTYPW1CR\nJcgSESdxnuRKIhJWI3UMIbliT2i6KqOiyzgyJ08KAgAovxHlS65saoK6qljBExcHHRPUYcQwSq48\n3wMlHqivYGDNvmlGbSpBrhLagodBx9WKfpJchcpV+cgVpRS3ju+gpTVwzljGuZYBXZXxoEhyNaRc\nBeRqDqF2lzqg/jC58ji5irmct2oosP05kSvPB9zF24IAWFO3F284JNefSwgu1y9h3zwQ78HDJz00\naxrbfZcDHvV24VNf5LsquoKN8+ycDXvGkl2bLI9HHU7agoosYaXJjvvNYI9nGScGe8IWVNE1y0de\nbOesiuGpRRhoz65cEUJQ0RXYNH9yBTBrcOCa6Do9PNjrQpYINtfr4uPNmo5uQFAmrqHh5IrkV3rn\n+R4sPx25OuxYoMG0YJS4iKdMT0E/BrmKTq9V09qCYzquOMIi0fLZgvvmAdp2B9db15hCQQgurVax\nc9BjVQAFwOIN7ZqMijK/lnaXOoCnDJMrhx3TlMS7eVR1BTbYay16WtB1fYDKkImceiVTbpAdsR5q\n3uDW4Hb3Eexg2OLiSn7vPbcceb4LAG5cDuz9lIF2bgs6NhHRBEopdg/7WF+piOlGTq7KGGoXtqCn\nljNz5foAJSAgZ+TqaYNEJCiSkotyBTBbyqH5Z66AyI7B/hM8eNzDxXNVqEoY+G7VNGE9TFSuAgk2\nT1tQ2B0pbMHDDrMTgWHlygzIFfUUDKzZRFDsuovYVEnJ1dGUzFWZbcH3j+4AAK4vXRP/trFah+tR\n7B0Wo5YMKVfcFpxDqN2hNuDLsOzw+HVsdpPzEO/YY6uV5jM55wTklnWvJQwk5gif+qCSA9+Jl2HM\nG9E1ODsHfVAAF1fTtd6Pg5gUbIQ2440NFplIG2jnD9zUlxkJANDuOxhYHtYjK3pqahWGrOPYbqf/\nBQpC3wmVq96gfOSF7ftla9nObMGnEJqk5jYGWtUVcZHPXbkK9pvdevIIluNhc60+9PFWTQOCHX8T\nm9I5uSL5kSvepUI8Lbkt2DYBKkGXDLQjBalCBfHkeLagE1YDpFWuRIHoGOUqbaC9Y3fxv3/7N4b6\nyfLGreM7AIBnW9fEv4nc1ZNirEFBrlRZlLwWXSTq+R486oF6MiwnPCYcix3TVszActVQ5tZWzvOC\nFbmy0MyV6VoAAairFKZmTkN0Dc6jfXbD51PbeeBB5yEICC7VwiJSHmoXgfbE5Cq4J/iyiB3sijD7\n8HFTU2uJM57zALcFy5q5shy+iF2BfaZcPX3QZC23EXtDV+AF9kRRytWdgx0ArJk9imZNA5TpO/74\nKo40i0wngXcG6VIlsXJ10GEkqq7Wh1ShYeUqji0YkitDZK6SBtqPUVOqYsghirTK1RuPv4NvPv42\nvrH3rURflwS3ju9Ck1ShDgARcvW4GBuT7eaUoMiSWE9UtHIlpr18WVyUAcCymHIVl9xVdVWE34su\n1HGFAmoAACAASURBVHQDclVVKxi45kJUIyBUl6mnYmDPfw/eWmUVqqRgu/MQj4Kl4pfO5aNcUUqx\n3X2Eter5oXP3/FIFzaoqohBZlCs+wCFqGEaWS9fUKnpOb2F/30noOwPIRAZ8uZTkiilXgCIpcM96\nrp4+aJKamy1Y0cIm5Lg78eKC1zE86rE9VuOUKzJDuaKiEC9/5aqiMHKVpBn8MCBXS3oDPacvFqCK\nUsqYmSsroqRUxbRgfKWAUopD62isagWwi6dEpMSB9r1g59iRVYxl0Hf6eNjbwbXmFchSaBGLOoaC\nQu2W44ldmhV5PsubLXGzU8TfGwCsQUCuYpK7iqGIh5C81eVRcFuwplbgUz/xxFpe6EeCzeYCyJUs\nybhUu4hHvV1s7zPCn5dytW8ewvTMobwVwDKwP/PJa3j5BlOzkpIroYR6oXK1czhcw8BRU6twfDe3\n+0he6Dt91NQqCCHlDLQHDx+KpJxlrp5GMOUqJ3KlKyCyC13WYzeLx8Wy0YIiKTh2WJ/KeHI1Xbny\nA+XKT7HIdBLEuh21Bs+niYKThx0LNUNBywiUocAaFMqVH88WjGaAKmpyW3DgDmB7NpaN8U3REpFQ\nV2uJbcGQXB0n+rq4uHV8F8Bw3goAluoaaoZSoC3oCnIllKs5katoFYPr+XCCaa64LfFVXQGRHahE\nGyKkRSCqXAFYWKg9OpJvxjifisDlxkW41MN2ewe6Jg8tas+CcXkrjk9//yZ+4uVnACSvYhBREV+K\n2IK8nX34+sofZvmDZlnQc/uoqlXUjJIG2oPzWJWUs/U3TyM0Wc1ltyAQLG9WHOhSvqoVENQxGCsw\nSRutmia6rTha9dmZK98Lwr80v6dXTq6aGvuZSXJXhx0Tyw0dDWG7BeSK34Ri24J8ek0RSkqShvZw\nYfN45QrgK3CSKVe7glwdzfjMdBDkKpK3AthT+8ZqDbuHfSG95wkrKGwFIKoYim5p5zdHQsNpwb7l\nggYlonGVq2qgXKkk/3N0FI7H9nhy+3FRuavQFlQWolwBYah933mMiyvVEyum0oJP6W3UT5IrIFz8\nnryKIVRK+fVl97CPii4zuzEC/jBbptyVT330nQFqSgX1SjkzVzyTqErq2eLmpxGapMGnvrCkssDQ\nZRDZhUaKsRuW9RVAdnBp/eRIdaumz1auPN4pkt+BzC8oSwZT0uKSq4HlYmB5WG4YaAaBcUGuIpmr\nWLagEypXqqxCkZREFzpRwzCFXDW0OizPjm0vuL6LffMw+P5FKVd3QEBwvXXlxMc2ztdBKUSAOE+Y\ntndCuSp6v6Dlsvddhir+3gPTFaP2casgqjoLtCuYA7lyfaiyJJSr/oJuvgOu4rqqIArzBic/1DjG\nxZzyVkBEuZpIrtLV7UQzfqbtwacUuwcDrC2fJIb8YbZbIuXKdE1QUFTVKuoVNi1YtkyYHWQnNVmF\n45fv9cXFGbmaAN5YnId6Zahsh5dCiinr032m8CyvniSCzSBzRag8NpQNAL6bP7niF5TlWtAF1Y33\nPvIw+0pTF9N4nFxFe64S2YJqkANSjES2IC8QXZpgCwLJQ+1PBgdiJUzH7uYue3u+hzvt+7hUvzB2\neKKopnbH9eH5lB3rAAw+LVhwiSg/PxUSZq76lhtMwJLYypWhSyCyB4UWX6jpeBSKIolsV39RtmAk\n0L4o5WqjzrJPUrWd66TgdvcRGlodLb0x9uNcuUobaIcvwXI8HLRNuJ6PC2P6ucqoXPHMaS0gVz6l\nsa6l84QdqOqarIKClnI/YxyckasJCPcLZiccqu6DEEBBMRduarEbZrV58kbSqKqA6kDyJ/9sL1jl\nkGd4kF9QVmusVyaucnXYYTea5YYeKelkxEWoEL6csOcqQq4S2IJcuVoxlid+TtIiUW4JchznHGq/\n392G4zsnLEGOjaBH6EHOOwatyOobAHNbf8NvjirRQlvQdAEQKFBjZ2oklR37ZMp5khdc12PKVUls\nwUUF2gF2nFRJC1KtM5agpEHf6ePAPJyoWgEQD5ppyJVCFAAEpu2dWHsTRRnJFR9iqClV1Ax2rpbN\nGrRdHwRhmfdptQbPyNUEhPsFs/9hZZVduOSCnor7bfZ9iXHyJFZktpyVulPIVZC5yqvXC2AhTgKC\n8w1OruLd5A7b7POWGzrq6mRbMNH6G0GuKqmUq3EFohxJV+DwMPt6dY39jJytQVEe2ro69uN8/Ufe\nyhUPRIvMlcIzV/MJtKuSOqxcgRGuuIF6EkzzYg5t5Vy54hOsgwXdfPuRBb6LVC80dxlEcVBv5fMa\nwmb2yeRKF+Qq6W5BRzgQluNFOq7GKVflswU50auqVdSCNUPdkhWJOo4PVZWgSoz8ndaJQWXWJ9y8\nebMK4NcBrAMwAPx3W1tbvxf5+I8B+IcAPABbAP4GgB8B8K8AvBl82re3trb+dq6vvGBoOdqCkhI8\nFdNiLtwHjxVgAxjg5I3a9V1AduH3J/9srlzlaVF1nT6qagUrDXYDidsKzmsYVhoGmgEfbItAO/uY\nAi3e+huRuWKHOR+N7thdQYqmvhbrEAQES1PIVVJbkCtXN5dvYLe/l/vE4Ljy0CjqFRWtmpY/uXKG\niawmqSAgc1CurODnabBdHz6l6Afj5Zqkw/TiHXc0IFfUnQO5cn00q5HM1cJtwcUF2gHA69aBFmBK\nBwCyL20O81YXJ36ORNjNO+nDs+XZYvesaXs46rLjb7wtyKcFS6RciSnuCtyAXPVKVsdgux40RYYi\nPf3K1c8C+NrW1taPAvhLAP7xyMd/FcAvbW1t/SCABoCfCv79j7e2tj4V/O9UESuAXayBfJQrBOWE\nxJvJZRPD833s7LBdTPvmwYmP8xPbtRUx4joK12HkKs8+lp7TQ02tYrmhY22pgrfuHsaaUDuI2IKN\nEeLCb6SGoieaFuQ3/A+0rgMA3j54J9bvcGgeo6nVp47mC+XKiq9cSUTCB5bZa8mTXFFK8f7xHbS0\n5lQrc+N8DfttM1e1Ilp7AbDJREMxCq9i4BkYoTQ7niDeuqzFDtR7hH2e7xRbwwAEgXZFQmXRtmAJ\nAu2UUnT2Gcl81N/J5XtudwLlakwNQxSarMVu8OewfVs8eFu2Jx4ax9mCdWELlki5Co61qlIVC7JL\nZws6PrSnQLmaSa62trZ+a2tr6x8F/7kJ4MHIp7yytbXF/+0xgHM5vr6FQRV+bw51DAVaDrsHA7ge\noNMGHg+enPi4kKQn7PijlApylZdy5VMffXeAuloDIQQvP3celu3hrTuHM7+WB9qXGzo0WYUh62HP\nVXCjrqhGbFuQEEBT2GH+4urzAIA3978b63c4so6xNKFAlENkrpz4matzxjLOBeQnT3L1ZHCAjt3F\n9aVrU0faeZnowxz7rix7OHMFsFB70Q3t3BYUAWXbCzJX7N/c/5+9Nw2WJDuvw07uWcvb9+7pnunp\n7qnZMYPBAAQBAoIIUATNRSYZdIh0wJJF0hapLaxwWBGSfsj+YYfCMh1yhIPBsIJ0mEGGYNMBErQA\ngRRJcAElABzMABhgqqe7Z6a3ty/1as3dPzLvzayqXG4utT3W+TPT79WSVS/z5nfPOd/5HItpcdY8\n+dLUi98ABeE4DkzLhiRwviw4Mc9Vx/UPOcLEmKvzto5uw/MBtpKHHH/35Bb+Yv/1vpmjg3jQegSJ\nF2nAchQUQUndzapbBt14a4aJvZMOFsoSyurw2j6NnitS6BFDOzB9xZVhWpBEAZLgXouzmnXFvJLU\narWvAHgMwA8Hf16v18+93+8A+AEA/wzACwCerdVqvwNgFcA/r9frv1fUQY8DRRraHd59DdssfuG+\nf+AuMsvSKvaNdz3a1++QaQd8FY22jvXl/h2WZTt0iGlRzFXP7MF2bLq4vPzUOr741Xv4xtuHeN+N\n+AXvtKmhpIhuNhhcZoga2i0NEi+hokg4aSQviiQagBQalyrbWCut4LvHt2A7dmyga1Nvw3IsrMbE\nMABBWTC5uOoYHbSMNh5fvIJl73XPesUVV0mSIEFwxuD1y9GSZxpQllDymR9VVJgZvaygbKYgAzCg\nBZirkqQCXTcOQpLjrz3CHhnaiANELbetXBKDUQyTY65KYglNYGKeq0fHHcBQIEGl2VRhsGwLv3X7\n8/jyg6/Qn12u7qC2cgO1lRu4sfwkVFGBaZvYbe/jseqlxDBYWZBTZdTZjg3DNqCIbnHV7pk4Ouvh\nSW8YdNjrS7w0VcxVJ9At6BBZcMqKK920sSzyEClzNV3Hxwrmu329Xv/eWq32EoBfr9Vq76vX6zR8\nolarbQL4PIBfqNfrx7Va7W0A/xzAZwE8CeAPa7XajXq9HkkDrayUIYrsC9vGRniLbVFYPXNfX60K\nud9L3XNv7pwjF37cx637AIDH17axf/AuDMW9eMj73Om5bayOKcERhj9Lp2cAjltkiBJXyPHtNd1j\nWKsuY2NjAatrVSx/7k28cecYq2tVCHw0q3LW0rGxUqLHsVpZxq3ju1hbq8CEgbKkYqmq4p3dJpaW\ny5Cl6HPGtByUFKnvM7288xx+/+6f4ow/Qm39euRzG8fuUOVLK5ux38maUwHP8ejancTv7taR67d6\nYu0ynry8DYHj0bJbhZ0TD999CAD4wBPPYmM1+jWfv7kBfOEtnLT00PfOcjzyey4rubFWoc9fUCs4\n6Bxifb1aWDjkIPh33f8uVSsAzlCuqrAd971WqwvAOVBZErFRjf9M3KF7nRha/PWe929F/GDlsozH\nttfBcRwMLvzvMGp07R6W1EUcALBRzLWf9jW+eusIAIet0jYedN9FZVmkRSdBW+/gf/3zX8Ube9/F\nlaVL+MjVD+DNgzreOryDh61d/MH9P4HA8bi5dg1Xly7Dcizc2Hg88Vgqiorj7jHzMRP/4GLZ3TTu\nnXRgOw6e2FmKfI1FtYqu1aW/n8TfOQjrjnv+XdnaQMvrFrQK+tvnBTkG3bRRLklYrroMe2Wh+Pvm\nOMBiaH8FwEG9Xr9fr9dfr9VqIoANAAfe7xcBfAHAP6nX618CgHq9/hDAv/Fe4k6tVtsDcBnAO1Hv\nc3rKTp1ubCzg8HC0O2K94y62R6fnOFTzvdd5x2NeOij8uOvvuj6rnbLLCL396B5urD1B3+eRVyTA\nlHF/t4Eb2/1G7vO2Tgc3NzudQo7vXsMtIgRboq/34vU1/PEbj/AfXn+Ap66Es0E93US7a+Datv/3\nLXElOI6Dd3f30dY6kAQZgkc43Xt4hqVKdBdku2egpIh9n+n9l57H79/9U/zp7dew6mxGPvfugbuL\nVu1S4neyIFVw0j5LfNyt3Xvu47klHB+1saQs4bB1Utg58eb+25AFGWVjKfY1S4JbfLx973TocVmv\nrUNv8K6uGfT5giPCcmw82j+lPpWi0Wi7jCHv+Rn39ps4Pe967+8W3o8OT8B148NBD8/c2I1ul8P+\nwTn4kGKwiHWHSPOOZeP4qI2SoOK82xr5ejYI27HR0bvYKrnXwHlLy30MWb6ft731a9srrt54923c\nWL5Gf3/QOcQvf/PXsN85xPNrz+BvPfc3oIoqvm/jo9AtA3cb76J+ehv1k9uoH93FW0d3AADr4kbi\nsQi2O7tu/6DBNJaMsNO8x/STppClshj5XiW+hMPuEQ4Pm2O5byXhpOUy5b1zG4ZX6B+etCd+XOS7\nMS0btu2AA6B7xMDRyTkOuckeXxTiij4W5upjAB4H8A9rtdoWgCqAoLnnXwL4pXq9/kXyg1qt9jMA\ndur1+v9cq9W24XYaPsxw7BMD7RYsQBYk0oWpFy853D9oYmVBwWNLbnF12D3u+z2hpB1TQqM1LKUZ\npg14i0VRxkHynlXRT1x+/1Pr+OM3HuEbbx9GFlengQBRguAInJ6lYVFeoJJhVzNji6uebg7NKnt+\nswaRE/Dm8Vv40es/GPFMP+MqyXMFuNLgfojfbRD7NIbB/VstK0t49/weLNvKPc+ubXSw197HUys3\nEl+rpIhYX1IL9VwNZooBgawrqzey4op4rkqS3x7f0UzwHIeKx4CwdCzSzjnDjXQg51jRML2hzZLn\nAyynjAcpCj1Tc5O6xRJkie8bej1O7J645+BT61fx9ZP/gAetR7S4qp/cxv/x7f8LHbOLT179OH7s\n+qf7iiBZkPD06k08vXoTuO7K7rfO7uKgfYgP7byS+N7BrCuWYd2+v0+GLPJ0wPDWSnQ+V0Uq40FL\nnxpTdtvogud4d86t6h7/NMmCZPSNLPqG9mkbfM0KlhXklwH861qt9icASgB+EcBnarVaA8C/A/AZ\nADdrtdrPeo//DQC/CeA3arXajwGQAfydOElwGkG7jwowtJOsH10rNlas2dFx1tLx4vU1at48GLjJ\nU8+VEW5oNyzbS7MuTtv2hzb7i84zj69CkQW8dusQP/WJG6EykW9m9xe6Ba+duaGfQ7N0KKKCcqC4\nioJtO9ANu88DBACqpOLmynV89+SWa1iPiFk4ZZgrSI9RXsD91iP0TI2OfQkDKa42yxveay/hrmOj\nabRi4x5Y8I43T/B6RL7VIC6vV/DGnWM0OzoWyvnz13ohhvaS4A9vJt60okFueGXJM7QbFro9E2VV\npDdMlpT4YFp5p2eOrLgiNw/Ro19LUgmN9sFI3isO5POWpRJkUaCp2OPG7nEHq4sKri0/BgB46Pmu\n/uThn+Ozt34bHDj858/8FD6884HE1ypLZby08byrqzAgmHXFUlzpgeJKkQVaXMWFn/YPb47u4B0X\nOmYHFdEd1SNLAmSRn6qcKzq0WRJocWVe1OKqXq93Afx0zEOi7iY/kumIpgQky6SIYE3SDaT1ii2u\niJn9ymYVq+oyeI7HYaefufK7BaXQlHTDtKnnqrjiyutIkX3mShJ5vPjkGr721gEeHrbx2OZwzlQw\nQJSAMFdHXVc+UAWF3vjisq6CcwUH8dza0/juyS28efwWPnLpQ6HPJ+Geq4zMFeCya3HF1UHnEIog\nY0l2DbCkoDrtRRd5rLjjmdmjktkHcXmjijfuHOPRURu1q0UUV16HnhTCXI0w60q3dIi8iJLi3Sh1\nl7kqKyIUz8PJxFyRji5TQkczR9byTIfSBpgrwzZg2Ca9mYwDJKm7LJagSDyd5zZOdDUTp00Nzz2x\ngq3yBkRexHvNB/jsrc/hyw++gqpUwc+98Jk+mbBIKDQomm0DTTbakiBBlQU0O+56uRESw0CQt2NQ\nt3RYjhU6yioL2kaH5m8BQGXKhjfrAebKz7manuIvDeYJ7RHwmasiiit359zrFGvqDRZXAi9gTV0Z\nimMgFzVvK+HMlWkD4MBBKFAW9EcsBPHyUy679tqtw6HnAH7G1WqguCLDm8nnUgLFVbcXfbxhMhXB\nc2s1AMCbx/XI55/2ziBwAlPYKJ2BaET7AmzHxkH3CFvlDcrakZmFjQLiGMiw5muszJXXMfigoDBR\nGsWg+N+3QucLjq640iwNCi/Tok4zLMo8qXR+HJssKEAGwFHT+ShgWP3MVdm7aY47joFIka4sOBnm\nas9LN99Zq0DgBVyqbOFhaxdffvAVXKps47/9wN8bWWEFpB+BQywi7vnmrkGri0rfhmIQlT7mKj1+\n9c3fxP/0tX9VyPBi27G94spfl6slaapCRGlxFWCu5sXVBYMfxVCALGj2AIdHT3NgFzjhO1hcAcBG\neR0to903P69ltCFyAhZKpQjmyhvNA6Gw8TetQJZKEC8+uQ6B5/Da2+HF1WkzhrnyGDlVVFHybuDd\nmOBDzYgurjbLG9gsreOtk1uRF+5p7wzLyiKT0XWRIUj0tHcG0zapJAgEmKucxZVpm3iPDmtOljcA\nf8bgw4J8V7SYHYhiADDSIFHN0iF7Mg3gspmaYaGsilBSvH/H6ELmFPoao8Igc0UYie6YfVe+LFh2\nZcEJMFfE87fjnYtXFlxp8Pm1p/HfvPILWC+tjvT90w5vDgbWknUlzm8F+GtgKyNz9aD1CEfdYzT0\n/DNINcv12VUC3ZjVkjtXkngBJw0iCwY9V7OaczUvriJQpKG9a/YgOBIArlDj6P2DFmSRpxc48V3t\ntXwPB6GBlysKGm19aAdEdtICxMJOYt9zVen7eVkV8czjK7i338JRY/hm4hvaA54rr3AhXjJVCHqu\nor9L8j2THeYgnlt7Gpql487ZcAOrZVs415s0iyoJLMObB/1WgF9c5Q0Svd98CMM2E/OtgthZK4Pj\ngIeHxQxwHhzcDAAlgXieRisLKoLPXJFzqBxgrljev2N2oPDu8XZiGNG8GDK00xE44y2uun3MFQ/d\nsAphR9Jg99hdJy6tuevXf3LtU/gvn/tp/Fcv/k3mTUIeyCllQS1QXJFiPmymYBB5ZEHHcWjG3257\nP/XzB9E2/HR2enxTlnUV3HzMes7VvLiKQKGGdio5FBfWZ1o2Hh21cXmjAt7LjdoouU6RvZbPDJEx\nNEsVGYZpDxUk5GQWOLFwWbAqDS88Lz/lFhffuDXcXXdyrkGVhT4zMWGFjrwuSEVU+roFozA4+mYQ\nz61Fp7Wfaedw4GBFZfNBLdDiKpq58jsF/eJqpaDiKq3fCgAkUcDWShkPD9uF3FR7mulOspf8JWU8\nzJUGRfClmTOvuCqpov/+CcWVZVvQLB2qVwyOhbkakAXHXVz1GdolAQ4wdvZi14vv2F5zN2FLyiJe\n2XqJiS0uAkpaWdC7ycuCTBna7Ri/FTBoaE+HntWjG94iiqtOSKPRtKW0E3lalgRKcMxlwQuGIhPa\nu2YPkuf7L6q42j3uwLIdKgkCwGaZMFfujdyyLXTNHqpSBUtVdyFptPtvNH5xJRRnaDc7UAU1NBLg\n5ZvuMX4jRBo8bfaGohMUQYHES/QCKwmsxVW0LAgAN1aehMxLocUViWGIm88XBPVcxRRXBx23mAwW\nV4vyAjhwOM2Z0n7X6xRMU1wBru+qo5k4a+XfQPQMC0ogDR8YvaHdTcw2IQsSZRJOW8PMVdKIE1po\nEIluhMwV7RacElmwJJboeChtzNLg7nEHFVXEYnn0w7LDkNrQTpgrXhoLcxW0GewVwVzRJoZgceWu\npVNTXBnDhva5LHjBIKW88KJg2iYM24DEe8VVQbLg/QP3wruy6be4E+Zqt+nKguRiqkhlLFbc9x80\ntfczVwV5rvT2kN+KYLmq4PqlRdTvn/Vd0Jpuod0z+8zsgDsAeDFgKg9GMbB0C0YVVxIvorZ6E/ud\nw6EOSz+GgY25WlSSR+AceMxVcN6ZwAtYlBdyMVeO4+Du2btYVpaYOhuDoL6rAqTBnm4NdWYSJqjL\nEIWQBcG5gkOyoCoGcrbYiquK7A1SHiFzRWVBYbKyYJDFIN9d1GD3UcC0bBycdrGzVhlZen8S5EAU\nAwuCUQxbq2XIIo+rWwkp8B5z1crAXAVtBsXIguRvHvBcqYS5mo4ChjJXAc9VUV7gcWNeXEVA5ARw\n4HJ3C5JOQdUrrnoFLdwPDtyLNchcraor4DmeMlf0YpIrNGxz0NROPFdBdigPHMdB2+xQOjwM739q\nA44DvP62Lw0SxmFlcdhrsRDISFJTMldhUQwEVBo86WevCHO1wlislMUSeI5PlAWXlaWhqIZldQkN\nrQHbycYaHHaP0DRauL4UP6w5DI9tuOdOER2Dmm4NZ4qJ7N16md7Te92g56rhsXBlRfS7FZOYK481\nWiDF1RiYq0FD+8RkQc9zBfidWuPA/mkXtuNgZy2e+RklUsuC1HMl4dMfuop/8Xe+d4hpH0Q1D3MV\nWE922we55fswWZB6rqakY9AwwroFp+PY0mJeXEWA4zjIggQjJ3NFWqyVgv0chLkiN0gAEHkRq8oy\nLa5aOklKL/vFVSucuRI5EbZjw7Lz7V5124Bpm5HMFRDwXQWkwdPz4RgGgoVAXpbbLZiiuIowtANu\nVxIAvHk0UFx5Mh2roZ3neCzKC5GGds3Scaqd9ZnZCVaUJZiOlTkH505GSRAIDnAugLkyrD4zOwDf\nUD4iWXAw1BEA7cYtq2JfSGQcSKGxqLrX0liYK3HCnqugoV0cP3O1SzoF16I3YaNGWllQs31Duyjw\nWIyZDuG/hwKBEzJd34QJFzkBXbObu2OQGtqn2nMVNLTPPVcXFjIvF8Zc0bTowmTBFtaXVJTV/hva\nRnkdjd45embPlwXlCl0Izjv9C4lJTuaCdgmkoIsrrrZXy9hZK+PNd06ofHcSEsNAsCD5zJUiKO6F\nJ/BshnYlmrlaUZdxqbKNW2d3+hZYn7liD/ZckKto6s3Q3eWh57cinrgg8nYM3j17FwDw5DJbvlUQ\nmysliAKHR0fZCjsC23GghcmC1HM1ellQFnkEebuyIoHneMiCnPj+Xe/Gt6gQ5mqEOVcDCe1EFpyE\n50riRUiCRIefjzOOYZdmXE2SuUoXxUDkKZlnD93lOA5VqZxRFnQ30CS7bi9nkj8Jjq2EdAtOT3FF\nZEFhHsVwkSELcm5DOymu6CJawK640dJw3jH6WCsC4uk57B6jTQodsewb2iOYK6mgzoy2mVxcAa40\nqJs2vn3XTV4PG31DEPRcETakrAjoMEQxDEpVg3h+/RmYton66W36s7PeGWReGgpBjcOCXIVuG6Es\nSVinIEHu4qrxLmRBxuXKTurnCjyPiirlLib0CH8b+Vt1RyYL+kwCGedBQDYdJUFJ9Fy1PdaoKpeh\nyMJouwWnhbkyu/S9iSyojTFIlHQKkoyrSYB6rhg7woPnWxpUpEpG5sotrp5auQ4gv++KHMM0M1e+\nLMjPQ0QvMmRByh3FQIqraoHFFQkPvboVUlyVXVP7YffYj0Rg9FwB+XcJfgxD/KL5/gFpkGZchcqC\nAc+V5+MpKWI8cxUz/iYIP5LBT2s/0c6woi6n8jDFZV0dMBRXWToG20YHe50DXFu8mnnwsyIL9LvK\niqjOTIEXIPHSGJgr99wO/q1J04MiJhdXnUD+T1kRx+K5Gkxo74wwriIMXaOLkneTnYws2IEk8lgP\n8ViOC4SB0sx0nisldXFVRtfsprZcnNPi6gaA/MUVZa4GQkSB6cm50vqYK7Lhn45jS4t5cRUDIM+0\nxQAAIABJREFUmS+Ouap6kkNc8CUr7g0kswdBOgYPO0d9SemqLEKRhMgoBlkoZgJ5m0EWBIDHtxew\nsqDgjdtHsGzb91wthhVXQebKXYwTiystvluQ4NriVZTEEt48fguO40C3dLSNDtPA5iAWY7KuRsVc\n3fXyrdKEhw5ClYTcwbZaTOyFKipMg5OzQA/IggCgBDK2iC9PFRSGKAayoy+hrMafV3kxyFwJvABZ\nkKk0OQ7Yjt3HXJHvbVyyoO042D1pY2ulTDP6JgFFTJdlGMy5SgPaMainkwbP9RYkXsTjC4+BA5c7\njqFtdMBzPF1DAXcTwmF6iivCXAVDROey4AWELEgwbCNzJxfgG9oXSHEVM7KFFXTsTUgb8KYnCx50\nj4ZYpKWKPMxcDciCeU/kljnckRIGnuPw8s11tHsmbt07w2lTgyL1B4gSDEYxAO7N0zDtyODDsMTw\nMAi8gGdXn8JJ7xS77X06imY5hd/KPcboOIaDzhFETgjNzSK+rizF1R3qt3oi9XMJFNktrvJ0IsU1\nD5QENbG4yQoiwZKbnRIiC6qCCt02YlmDYOZTWRHR0cxCx1QFYZru65IoBsBlr8bJXJExKL4sOF7m\n6vRcg27YuLQ+Ob8V4M4IBNLnXKUdsE3Wwmbq4qqJBXkBkiBho7yG3fZ+ruu0bbgFdZCR53kOZVVE\na4RsbRr4IaI8OI6DyIuFzPedBObFVQyKKDgIc7WouhdYEVEM9/abUGUB60vDlPpqaRUcx+Gwczw0\n42+xKqPZNmDb/gVKiiuloHE/UaNvwkC6Bl97+wgnTQ0rC0qoFEdkQYHzTY5JWVeaV8TGDVUlCKa1\n+xlX6ZirqCBRx3Gw3znEenk9NHl6SV4EkG2+IB3WvHg19XMJVFmEg3xt+HFp+IqojNxzNSgLcpz/\n/4qYbFqmo2Akt7hyHBQ6pioIOrhZHCyuxue56gQ+LxAorsYUxUD9VhPsFATc7mqe41PkXBmQeCl1\ngjzZ3DY19q5cx3HQ1Ft007ZT2UbH7MaO2EpCZ2BoMz2+kjQ1nqtmxz0OIldKfHFj2caNeXEVA5nu\nbLKfeIS5Wiq5N9+8IaK6YWHvpIPHNqvgQwoRiRexXl7FkcdcBWngpYoM23H6LiSy2MseRW7mlQVT\nFFe1K8soKyK+Xj9Aq2tEZsYQ5iqYEZUUx9DTLQg8R+WXODy7VgMHrq+4ShvISYc3DxRX53oLPasX\nKgkCbgFflSo48zoUWWHYJt5rPsDl6g7tyssCUoTkKSbi0vBVQYFu6bnY3ygMemBIIV1WRHpt0JT2\nmBtoMPOJMF6j8l2RQenB87IkquiZvZF8R2EYTKQnCe3jYq4eHU++UxDw4nZ4mblbULN1OpIlDShz\npbEzVx2zC8ux6KZtp7wJANht76V+f8DPHyyHNOlUSxLaXWPssyXDcNbSIfAcLa5Evrhw63FjXlzF\ngA5vzmFqJ8zVglKGKHC5/RwPj9pwHOBqiN+KYLu6gYbexEnvFBWpTNmgMFM7Ya5U6j/Ia2j3srUS\nZEHANfW+78Ya7WAM81sBrlwjcAK9UQIMxZVhJfqtCBbkKq4uPoY7jXfxyFu8snuu+neWBx23fTqq\nuHLfawlnvUaqxe1RaxembdI27awg3ZR5TO1xafijHIETDBEF/OIqKC0T5qobI7t1zC5Kogqe41FW\n3Gt+VB2DphUiC0olOHBGlgc2iGDGFeAzV9qYiqu9KWGuAJexTxMimiaGgYAUV2k8V4QB95mrLQDZ\n4xg0S4Pt2H1mdnp8JQmW7RQWE5QHjbaGpapM71kSL8Gw5szVhUMR8wVpFIOoQpXzm2Xvx5jZCXaq\n7i6noZ/3de35xZW/iA/KgsUxV2y70pdvBgYZh8QwAO4O8/n1Z/D06k36s5KXXxU1B07T2YsrwJUG\nbcfG1/a+4R5LSs/VQoTniswU3CwNZ1wRLKtL0G0jtgAYxMPWLgDgsWr6CIYgCHOVR66OS8MnrOko\nTO2DrfHk/YPZbyXv/WOZK8M3d5coczWa3fJgQjvgz3oblzToD232ugXHnND+6LgDjgO2V+OHHo8D\nisesskC39NSdgoAvC56nkAXPaXHlrvPbXnGVlbkKCxClxzclcQyO46DR0rFU8TfRc1nwgoIs2vmY\nK/ekdpPFhdy7g/v7pLiKnmm1veAXLMEiZ6nqnrTBrCsiUyhiQTlXRhsSLzF31Lzw5Bq90YTFMBD8\n/AufwU8//ZP0377nKvz7dGfdsRtPSVp703C/X9Z0dno8Hrs2KAvSTsFKNHNF3iuNqf2BV1xdrl5K\ndZyDIAVoHtYi1tAuji6lXRvqFvRlQQI6AidWFuzQ4oplbmUeDM4WBPyAYdK1OGrQ7kjSLTjmKIbd\n4zY2lkqQxGzxIUXCzTJMwVzlkAXTMFeEASfM1VZ5Axw47GZkrsICROnxqdNRXLW6BizbwXLVv3e4\nsuC8uLpwKIq5UgQZPMcnxgew4P5BExznjy4Jw3bVv5EHmavF8nBKu2HZEHiOeq7yDslsRZgmo6DI\nAp57YhVAeDp7FFg8VyxmdoIrC5exILm7xJJYGpoBmASO42hKexCkuAobfUOwrKQ3tT9sPQIHDpeq\n26mOcxDkO8rjudJi0vD94cnFM1eRnivVvwHS+YYRxZ1lW9AsnWY+VUbuuSKGdt8vSYqcrjGejsFh\nQ/v4ohhaXQPNjjFxvxWBIrieqyRJ3nZs6LaROoYB8IurLMwVYcQlQcJGaQ17GTsG4xSFask95ycd\nx0A2/YQEANx7cF41ZVKYF1cxkIT8BUfX7NHhrCVZRE+3+rr10sBxHNw/bGF7tRxbOGwvbNL/72eu\nhlPaDdOGKBaXhts22qmKKwD4oQ8/jpdurOOpK+xsUVxxZVpuREMaWZDneDy7VgOQ3sxOsChXcT4w\nAuegc4iKVI4NVfWzrthM7Y7j4GFrFxvltUwyRRBUFizC0B5yTrIOT86CoW7BGOYqqmNx0NxNDe0j\nYq4M0wbPcRD4oOdqMrJgaTCKYQwJ7dPSKUggCzIcOInrHpGmsniuyLXfSmFobw4wV4Dru2qbHcqu\np0FYOjs9vimRBc88u8ogc2U61tiaPYrEvLiKQTGG9i6l/UlBkPVGdtTooatZsX4rANisrIPzJq1V\nQjxX5wOGdkngC0nDNW0TmqUnprMP4sblJfz9n3wxNOMqCsQbE1ZcxRms4/D8+jMAXIN5FizKCzBs\nk0pQlm3hqHeCzVI0awUEiivGlPaT3hm6Zi+3JAj4BVEuWTDW0J4sy2XFUM5ViOcqibnqGP0SGSnM\norx8eWFYdh9rBfiyYHdcxdXAZ/YT2kd/A9udkk5BAtbh3llH3wAue8uBS5Vz5TNX/lq/TU3t6cNE\nfVkw3NAOAO0JZ12RTf9yNei5KiZ/cRKYF1cxyCsLOo7Tx1wR2aSXMUj03n6ymR1wi0Jyww6ySAvl\n8G5BqSDmKq2ZPQ/ivDFajME6Ds+sPoWrC5fxwvqzmY7JN7W7C+NR9xi2Y8d2CgJ+McfquXrYegQg\nv5kdKIi50qK/b2IoH8UIHN3SIXACTXIOY67UBM+Vb+4mzJV7zTdHtIs3vc1MEOOeLzj4melswTF4\nrh4dTX6mYBCE2UzyXZF7QBammOd4VKRyqpyr84FuQQDYrriKxKMMxdUsGNrPWu41SkgAADM9X3Be\nXMUgr6GdJCEPMldZfVf3D9wLLs7MTrBRdrvTgiySJPKoqGJ/cWWR4io/c+WHlo5+4Yz7Lrs0dyld\nknJJVPHfvfoP8NHL35PpmBZo1pW7iMaNvQliicwXZC6uiJk9f3FFvqM8N9a4NHzKXI2guNIGurfC\nohiS3n9QFtxYVqHIAr7y7V0cnRVf7JDrLQiy+SJeqFEjOEsRcCNRBJ6jfrBRYtqYKzq8Oam48u4B\nUgZDO+CuiWmYq6begsxLfd7PnYrrr8wSx9CJ9VxNS3E1zFyJtLiaPd/VvLiKQV7mirTW0+JKJgVB\nthtZ3MDmQWx6MwYHL6alqoJGy9/Fm4S5EvKfxP64ndEvnHHFlRbjARolBucL+mb26BgGwC0ASmIJ\nDe2c6X0e0BiGAmTBQjxX0QntJIphFCntmqX3yTQ3HlvCtZ0F1K76njk1IYph0NxdViX8zCefQlez\n8Cu/+x1YdrEFh2HadGgzAXnvscmCZhciL/Z1vskSP5Zuwd3jNhYrMu1QmzQU1uKK+PsyeK4Adx1u\n6W1m79C51uxjrYBgx2D6OIY27RCNLq4mb2j3mKtqkLmay4IXEnkN7X5x5RnaSTZTRlnw/kELC2Wp\njzaNwvs2nsdOZQuPL17p+/lSRUa7Z9Jd6qDnKs9JnCadPS/K5LsMLa680TcpZcG8WKQjcNwimGZc\nJTBXgCsNsjNXj1AWS1T6zYMiugV7ugVR4IaKBmDUzJVGZR0A2F4t45/9F6/iamDmZlIUg89c+Ted\nj7ywjVef3sTtBw38f195r9BjJjJ8EJOQBcsD3htZFKCNmLnSDQvHjR4uTQlrBQTUCcbiKovnCnAV\nBGITSYLt2GgaLWoz8I9VwnppNRNzFWfZIJ6rZie7t7gInLV1cJzf1Q4AojCXBS8kKHOVURYcZK5U\nObss2OmZOGr0cGWzGjp/bxDPrtXwTz/0j4Z2P6Qwa3bc9uMhz1WOzsjBWYajBJEywnKu4saxjBIL\nIcwVB45KtHFYVpbQNbuJXXU9U8NR9wSXqztM50ESfOYqX4holATLMn4mK1hCHZMN7f3MFeDGanzm\nB2tYXVTwO3/2Lm4/TD/3MQqmNey5Grcs2DVCiqsxMFd7Jx04mJ5OQSCLoT2rLOiuiWSCRRw6Rhe2\nY2NRGbZ/bFe20DLaoQPi41+zAw5caMSMIgnYXC7h9sPzsWWdhaHR0rBYlsHz/romF2BXmRTmxVUM\n5JzDjLu05dktrso5ugUfHHqSIIPfKg6LgRE4lu3AAQY8V7NhaOc4LjI3jHSvjZ+56je0H3QOsaau\n0MI1DiTrqpHAXj1q78GBU4gkCARmC+YKETUjC9lRjb8huUOJxVViFEN/5xxBRZXwcz/8LBzHwa/8\nzpu58+kIwpgrkoM3DlnQdmyXuZIGiyth5DfWR14Mw/YUMVcKK3Pl3dyzMlc0SNRIDooN6xQk2KFJ\n7elM7W3vbx41dPqVpzegGRa+dfck1esWBZLOHvRbAb7nai4LXjD4hvZiZEHSLZgloPDePjGzJ/ut\n4hDMuqKjOAqKYvDnCo5nZ1qOKK4m57nyhzd3jC6aRotJEgT8OIYkabBIMzvgy4J5uwWTiqtuwSGi\ngwGiURB4ARIvJjNXIV6U2tUV/NCHH8dRo4df/9KtnEcM2I4Dy3aGiiuO41AWS2ORBUmTzbAsyI98\n/M2eZ2a/NEXMFcmt0hLUCSoLZvRckTWRhbminYJSccVVx+iEprMTvPq024n49Xq2BPi86PRM6Kbd\n57cC/G7BPEHek8K8uIqBb2jPKgv2M1fE0J5FgqEzBRnM7HGgWVcdvW/OmThjUQwAopmrjN2CuY9H\nLEHkBJzrLRx02ToFCZZVtjgGWlwtFFtcZfVcOY6Drm5Cjcgok3kJHLjCmSvNYmcSFEFhjmIYxI99\n9Bqu7Sziz9/cw5dfe5DxaF2YJJ09xJs2ruKKFJOlgRutLAowTBt2hvRvVjyask5BwB/szWxozy0L\nJjNXNEA0RBbcyZB15TgOOkYnNIaB4PGtBawvqXj99tFEpMGTc3fztTxUXM0N7RcSrGbHKAwb2rN3\nC947aEEUOGyv5luYqCzY0vqKqyI8V+0xeq4At0FAN206r42gNyFDuzsCZwFNvYX9dvLYmyBY5ws+\nbD0Cz/HYKW/lO1gPPM9BlngqpaaFbtpwnGh/G8dxUEW1cEM78cgEDe1RUAUlJorBvdmRDdAgRIHH\nz//os1BkAf/7b72RK57BsIaHNhOUpBK6RjfTaJM0IAVcJUQWBABjhEGiu8dtKLKQaszVqEG6/0Yv\nC3op7SmYq0FDOxDsGGQvrnTbgOlYkRsIwL1OX316E5pu4dvvjF8aJMVVcGgzgEI2/ZPCvLiKgUQN\n7QVFMWTsFrRsGw8P27i0Xgnd9aYBOXkbbb1vsRd4ATzH52aueI6n7e+jRlQcQ9aE9iKw4I3AOWDM\nuCJYYZAFbcfGw9YutsobmfN2wqBKQmbmioUlVGOYo6xI072limpsFENJVCO9KACwtVLGz3zyKXR6\nZq54hiTmynSskRt3fRl02NAOANqIRuDYtoP9kw52VsuFNGIUBdacKy2nLJiJuQoprmRBxlppNVVx\nRTe9Yrwc+4EJSoOnkczVPOfqQsI3tOeTBcukWzBjiOjeSRemZec2swO+LNhoBz1XbhGSd0hm2xva\nPK7FsxzxfU6qWxBwF0TTNvFe05WQkjKuCIjnKs7Qftw9hWbphfmtCFRZzGxoj8u4oq8vKpGep6wY\nnCsYB0VQIofzhsUShOEjL2zjo++7lCueIcgUD2JccQy+DDosCwIYmSR01OjCtJyp6hQE2HOujBQy\ndBiqKboF/XT2cAvITmUzVccgSWcfZCsH8cS2Jw2+fQRjDHMmgzg5JxlX/czVXBa8oOA514uUl7lS\nBz1XKYur+wWZ2QE3MI7nuP7iylvs3c+a/SRuGe2xZFwRRMmsdPzNmA3tgL8g3mm8C1mQmbOoSqIK\nWZBj5wv6Y2+K6RQkUGQhs6GdjL4pxTJXKrpWr1DJK5UsKCpw4ITeQFmLK47j8Is/+b5c8QxJsiAw\n+jiGqO5IxWOuRjVf8LjhroUby+NhtVnBav1IU8yHoUIN7Wm6BcM3035SOxt71YkZ2hwEx3H4QG0T\nvQlIg6dNTxYcYK7msuAFhsLLuUNEyUImiTxEgafjWVhBzewFFFc8z2GhIuG8pdPdiegt9lIO5sp2\nbHTN3ljS2QlKEfMFJ2VoB/wFUbd0bJXWmVk8juMSg0QfFNwpSKDIriyYpfhhZa5sxy50gWTtFgSC\n8wX7fVeWbUG39MSbDkG1LPfFM6RleYwEWRAYA3MVkusF+J4rfUSMxamXvr08RX4rwC/Ok3Ku9Jw5\nV+Tvy1pcKYIceW5vl135bpcxTLRNhzYnn+dUGnxrvNIgNbQPeK7I9z2XBS8gJEHKZWjnOT/mAHB9\nV2llwaI6BQmWKvKQ5woAJCE7S9cxunDgTIi5GiyuiKF9/Kd30CfBamYnWFKW0DLakcW8H8NQLHOl\nSgJsL1A2LegcRyWuuPKyrgqMY0jDJEQFiQ7OFWRB7eoKPvLiDo4aPTw4ZJ8VB8QzV+QYRp111aEd\nzOGeq1ExV6dN97tfqU5bccVm/SBB0lllQYEXUJHLTIb2pt4K9VsRpI1j8Jmr5PP82s4C1hbdrsFx\nzJokOD0fHn0DBJgra85cXTjIgpRDFnQlhyB7UZLD4wPicO+ghbVFpbB5XEsVBZphodVxPxdJjHaZ\nq2wnMU1nZ9gdFYWyGm1od0364z+9g8F/aYsrYmpv6OEzBh+2HmFBqmIppEU7D0hXZZaOQZ+5imYJ\nS0LxI3C0FIb2qBE4aW46Qax67Etan1qSod09pjF5rkLG3wCj81ydNd2/1zR1CgIpBjdTQ3v2NXhB\nriQyV7Zjo6kPj74JYruyCQ5cClmQeK6S12aO4/CBpzfQ1Sy8OUZp8OS8h2pJGro2fM/VnLm6cJB5\nOdfgZnWgxVtVhFSyYKOt47yt40oBZnYCYmo/8nwQUkAWzCqBjjvjCoiXBSdhZgf6mSvWTkECGiQa\n4rvqml0c904LlwQBP2w1S8cgS/PAKFLafc8Vuyw4KP20Q+YKsoCm2qf8vlg8Vyyz5/KgmyALaqNi\nrqZUFiTKAnO3YEbmCvCLqzj5vW104MCJZa5kQcaqusLMXLXMdBE5RBr82hilwdNmb6hTEAh2C86Z\nqwsHl7nK3i1YHiiuyooITbdg22z+lvsHxZnZCQj1OlxciTAdi3lyexA0nV0epywYPry5p1sTMbMD\n/R0+WYursKyrh609AMWFhwaRtVgA2AztScOTs8D3XLEY2r2U+EFZ0Ag3dye+npRtZJBputf84GzB\n4DEQw/moEDaoGgjIgqPyXDU1iAKHhVJxESJFQRFkppwriRdjIzuSsKBUYTlW7HWQ1ClIsFPZQtNo\noaXHy4y2Y+PWyW0AwJK8yHScT+4sYm1Rweu3D8ciDWq6hU7PHOoUBOaG9gsNmZdhOzYsO92iY9kW\ndNuAOrBwq3K6+YL394szsxOQINHj82HmCgDMlJ8VCDBXY5QFI3OuJshcBel8loHNQazEpLQ/GFGn\nIFCULBhvaAdGIwvmYa6o/yilLChnLK5imSuvAByHoV3kxSFjtjJqWbClYbmqTFXGFYEiyEyyYNaM\nK4Kqktwx6BdX8UoFTWrvxLNL/3HvNdxvPcKrWy9jRV1mOk6O4/BKbdOVBt8dvTR41vZYzUo0czWX\nBS8gSFhjWvbK7xTsZ66i2JYoEDP71YLM7ECILCj4zBWQrTOjNeZ0diA858pxHE8WHH+nIODeJEVe\nxJK8EJn6HYVY5qo5mk5BoCBZMMbQXhKIoX0ysqBCi7v8hnYg+8gg0p0bbmh3r5uuMVpZsGN2Qj/v\nKA3ttu0N5Z0ySZBAZmGuLCOXJAj4fsy4rKtzLXpocxC+qX0v8jE9U8Pn73wBEi/hx65/OtWxvjrG\nrsFGy/3uw5grf+bt7DFXk7kDzRD8+YLGUIdNHAYzrghokChjSvu9gxYUWcD6crobQBxIcXU8KAvm\naHslu7HxyoLEc+Xf5EzLnY827tE3BBzH4dNPfDL1DRtIkgV3IXICbcMuEmnZ1CBYDO2jYa7YQx2j\nohii/EeJrydnlAUtVxYUhWH2hhzDOEJEF0IGAo8yiqHR1mE7ztR1ChLIgswUxZD2PBnEgkJG4EQz\nV00jOp09iO1KchzD7937IzT0Jj79xPczs1YET15axOqigm+87XYNhm0IisJZK7xTEJjt4mrOXCXA\nD5lLV3AMDm0mIGxLj2G+oGFa2Dvu4MpGFXyBdDqRBU1rOEQUyNb2Oi2y4CTT2Ql+8Im/io899uHU\nz6tKFYicMJR1ZTs2HrX3sF3ZgsAX/7mo58pI/3eflKFdTxUi6r5/VBRD2nM2syxIQ3uHvytfFhyd\n58pxHHTNXmiRIHtrwCgM7eTmOW2dggSKoMB0rFjrh2brY2aukoqr+DiGk94p/v29L2NJXsQnr/6V\n1MdKAkW7monvjFgaJMxVWPEtzsffXFzIOWXBQbaL3IRYmKuHR23YjlNYvhXB4HDMQc9VNuaKyILj\nY65kkYfAc+HF1YQM7XnAcRyWlKWhlPaDzhEM2xiJJAgEmJgMzFWXMaEdALojyLnKF8WQjbnKLguS\ncVPDyy6ZyTnKbsGepcF27AhZcHSeK5JxtTylzJWSsMY7jgPDMnJ7rhaYPFdszJUiyFhTVyLjGH77\nzhdg2CZ+9PoPUuY4LcYVKEo8V+HM1dzQfmEh88UyV1Em7DDcG4GZ3T0GoY/mDXYLAlk9Vx1w4HJT\n52nAcRxKSn9uGB19M0HmKg+WlSWc682+XbQ/9mY0xRUpFrLKghzne3bCQEM8C/Vc6e54Ki7576xG\nFVcZPVdZZUFiaBfFcBa6LJVGmnNFXrsUwtT5smDxzBUNEJ1i5gqIzroybBMOnMzp7AQLSjJz1WTs\nFgRc39W53hwq1t5pvIev77+OqwuX8cHt92c+3icvLWJlwZUGicoxCsR5rgReAM/xc0P7RYTvQyqG\nuSI7fJbiqsixN0FwHEd9V0B/iCiQbZfQ9oyyeVqVs6CkCH05V6TjbVKG9rxYUZfgwKFdQ0Bw7E3x\nnYKAXyxkK67czsy4LrCq5/GJm5uYFrqtQxHYus/USEO7uyEY9EUmIbcsGMJcAe5GbJQJ7f7Q5uFi\nUhGJob145mraZUGygY4qrgijlXWuIMGCnOy5OtebKIkqve/EIUwadBwHv/X25wEAP3HzR3Otx7wn\nDXZGLA02WtHdgoArDc6ZqwuIoKE9DaKYq2rZfb033zlJnOV2/6AFjgMe2yi2uALQX1wNRTFk8Vy1\nx9opSDDIXPmjb2aXuQLQ57t6OKKZggRKRiYGcL/vpEJ2Ua6iJKrY6xxmOr4waKbGfLOLjGIwulBF\nNfUNKKssaFLmKvz9ymIJPUtLHfvCim7E0GZgTLLglBZXCk1pD2dW9QICRAE25upcbyZ2ChKEFVd/\nsf863jm/h5c3XsCN5Ws5jtbFq2MIFD1r6aioIj0HByHNi6uLCWpoT0lLRkUxPPP4Cq5fXsTX64f4\n4n+8F/l8x3Fw/6CFrZXySAIxF/uKK/f1JSGbLOg4DtpGZyLFVVkRoRs2vXFpM+y5AsI7Bh+2drEk\nL46sEzOfLJicKcZxHLbLmzjsHhVWOGiWzlxciV74Y1gUQ5auTjKzsmjmyp8vOBrfVTvGY+aHiI5Q\nFgzx1EwDlISmpSJG3wA+cxXlubJsC22jk+i3IrhEsq684kq3dHzuzhcgcgL++o0fynWsBE9e9qTB\nW6OTBs9aGlYWo9ljiZdgZpwcMknMi6sE+MxVNllwUHIQBR5/9z99ASsLCv6fP7qDN24fhT7/uNFD\nVzMLzbcKIqhvDxva0+0SelYPtmOP1cxOQDxspDCYhm7BPFgZKK5aRhtnWmMkyewEhHnKwlx1NbZM\nsa3KJmzHxmH3OPV7hCFN9xbHcVAFZSiKoWN0MnkEBZ6HKPAZohiiQ0QBP8x0VHEMnTjmaoQhomct\nDdWSFNolOQ1Imi+op4j9iIMoiFAFJXJ4c8tow4GT2ClIsOXFsux5cQz//t6f4FQ7wyeufB/WS2u5\njpWA5zi8UtvwpMHTQl4zCMO00e6ZWI0trubM1YUE7RZMLQsS5mp4IVuqKvi7P/4CRJHHr3z+TTw6\nGr7Y7o3Ib0WPIdRzRaIY0n3WScwVJBicL9ibdUM7SWn3/EkkPHQUyewEeWQu07KZClmSz5WUKM0C\n27Ghp2CuAHeTE2SuTNuEbhuZmCvALd7TxhawM1cjKq6MaAM/z3NewTga5mpaOwWBoKH7ewD5AAAg\nAElEQVQ9XBYsYq4gQUUqRzJXrJ2CBKqoeDMG93CmNfCle3+IBamKv/bEX819nEGMMlC00SZ+vATm\nal5cXTz4smBa5spdyKLMstd2FvG3fuhpdDUL/9tvfRPtXn9BMyozO0GwuCLdS1kzRSaRzk5Auy97\n7sWnGbPNXA3KgqRTcFR+KyBoaE/JWKZgCUno4X5M6CEryC42zc1OFZS+m2fWTkECReKzRzHEeK4A\njKxj0De0h1+nisQXHiLa1Uz0dGtqzexAMMswwdCeM4oBSCqu2DsFCbYrm2joTXy2/jnolo4ffvIH\nUk+GSML1y0te1+Bh4dIg6RRcXYo+ZpEXU9typgHz4ioBVCrLyFyR0R9h+J5nt/Hp77mK/dMufvm3\n34Rl+yeuX1yx7WLSghRXAs9B4N3TQM4oC9J09gnKgl3KXCUnhk8zFuUF8BxPDe2kU3BUMQyAy1rI\nYnqZK813TYZYF8FcaSkCRAkUQUHP0mgTiZ9xlW1DIEtCZlkwytA+elkwvqCUJaFwWdDvFJxOvxXg\n51yNWhYE3BxAwzZCC7km41zBIMgYnDeO3sTl6g6+99IHcx/jIHiOwytPbaDdM1G/d1boa5+R4mox\n+lqWeBGmbSY2gE0b5sVVArIb2ruQBTkxUfsnPnYdL15fw5vvnOD//sM79Of39puoliQsj8gEuui9\nbnChz8pcTVIWHJwvSGXBGTW08xyPRXkhwFztQuJFbJTSDYFOC0UWUhvaWeYKEqypqxA5gfpD8kAz\n07fGq6IC27HpxiEvc+XKgqMxtI+quEoa9yOLfOGzBac9QBTwi/RI5orKgvkM7YC/RoaxV4S5Yu0W\nBICd8hb9/x+/8cMji8K5ecUdn/PoOLrTMQtYZUEHDixnNF20o8K8uEpAHkM7y8LN8xx+/keew85a\nGV/62n382bd20dVMHDV6uLJZHdkU+aWye2MKLvSykI25ak0gnZ2ADMImnistxQ1/WrGiLOFMa8Cw\nTey197FT2R7J2JsglAxMTI8hnZ1A4AVslNex3znIvQPNkjs0GMfQ8W5uWUNvFUmAYdqwbfbPwhLF\nAPhFUNFgYq4KlgWnPUAUYDC020V6rkjW1XCR0kzpuQKAKwuXAQAvrD+Dp1dv5j6+KBBmicyjLQo+\ncxUvCwKzl9I+L64SkMfQzhpOWFZF/P2feBFlRcT/+cW38OXXXZ/NqDoFAT+KQepjrrJJoNNgaB9k\nrmY1igFwfVe2Y+P22V2YjjVSSZBAlQWmeZdB+LIg23e9Xd6EZumhg6nTIJMsOBAkmpe5yhIkapg2\nBJ6LnBM66uHNHaMLkRep1WEQslQ8czXtAaJAcs6VVlAUAwBUGZirNMXVYwuX8Pde+jn8zWf/Ru5j\ni8O6V/ycnBddXHnnR4IsCGTLX5wk5sVVArIY2h3HQdfqDWVcxWFrtYz/+q8/B8t28Nk/vA1gdGZ2\nwL05lBWRDmwFsoeITtJzNSgLkpvdrHYLAn7H4JvHbwEYXTJ7EIonc6VhldLGXmwRU3vOMFH/Zpee\nuSJxDHk9V1lG4BimHWlmB/xpDqOMYiiLpUg2XBYFWLZTqGl5NmTBBEN7od2CJOtqmLki3YLVFLIg\nADy9ejP1lIG0WKjIEAUexwUXVw0m5ir7zNtJYl5cJSBLQrtm6bAdO/UJ//y1Nfxnn7hB/z0qMzvB\nz3zqKfzEx6/Tf5MdQlp/WXsaugU1P+eKAyLTfmcBpGPwzSNSXI2BuZLS31i7KZsHtgdyebKCFFeK\nmC6KAQgyV9GZTyzIxFxZNsQIv1XwWEYWxZAQmkp8ikaBQaIXQhb01v68428An7kKG4FzrjdRFkt0\nHZ4m8ByH1UWlcFmw0dIgSzxdx8MgC7MpC07fX3HKICXsasJAdsdZFu5PvXoFR40e7jxqYGdttMXK\nh5/f7vt3Vvq1NQWyYCfQLSjLQqT0MgsgQaIHXTdgdhzFlSL3F6ksSMtc0TiGnB2D5FpM0xo/5LnK\nHcWQPhssibkaZRSD4zjomj3atRkGmtJuWLE3uzQ4a2kQBR7VUn5JbVRINLSPwHMVxlw19WYqSXDc\nWFtU8d33TqEbVmGb17O2juVq/IzQrHaVSWNeXCVA5ARw4FKxOWRxzELVchyHn/7UU6mfVwTIsNAs\nzJUqKNR4OE4Meq403ZppvxUALCvL9P9X1ZXMpus0oCNwNBOsZWnP+85Zb8SbJI4hN3NFPFfsNzuF\nyoLE0B7fOZeELLKgadmxNyVJkCDy4khkwZ6lwXbs2GKSpLRrBTNXy1V5ZI05RYA5ob2gnCtgmLki\no28uVbbDnjYVWCO+q6aG7dX8G2nLttFs69heWYp9nDQ3tF9McBwHRZBhjIm5miR85iq9oX0SrBUQ\nEsVgJM+6m3YQWRAYD2sF+N2V3RRBommZK0WQsaqu5GaufFmQXWpSqaHd81xNyNAeFcNAUBZLI5EF\nSTFZEqOv0yBzVQQs20ajrU+1JAi4G2ie42OKq+KYq2oEc9U00ncKjhtrXtBnUdLgeduAg/5RbGHI\nel+aNObFFQMkQUrF5rAEiE4j/MDUtIb29sSKK1niwXNcX7fgLJvZAWBJ8RfYcXQKAn53JWGjWJBl\njuNWeQMNvZmrgMhnaPeZKw5cZiNwJlnQsiNjGAjKYmkkzJWfzh7DXElkvmAxzNV524DjTLffCvA3\n0FFNS9oYcq7OtfSdguMGYa6KMrWTjKulhCxHMWO49aQxL64YIPNyKkM7yakpSbNVXAmeBJqmK0O3\ndBi2OZGMK8BdGEuKgI5mwnYc6BdAFhR5kQYJjqNTEPC7K9PEMWRJwye+qzzSIJUFUxnaPc+VZ2jv\nml2URDVz6GImWdB0EpmrklhC1+wVnkbdZTDwk87hopirWegUJJB5mZ4bgyAb6yKiGGRBhsRLQ8xV\nlhiGcWOt4KwrknGVdH7MZcELDFmQUkUxdK3ZZK44jks9gXySGVcEJUVEVzNhGDYcAGpBZtxJgpja\nxyYLSjlkwRSBrVt0gHP2OIYsHpghz1VC51wSiITGylxZtg3bcWIN7YDLLNmOHZm5lBUsHjPKXBUU\nJDoLnYIEiihDi1jjdUuHyAmFBfmGzRckMQxp0tnHDSILFpV1RTKugnNuwzCXBS8w0jNXXnE1BiNy\n0ZB4KRVz5XcKToa5AlzfVVezKJMyq6Nvgnhl6yW8sP4M1kurY3k/wlx1U8iC5LFpZEESx5BngHOW\nEFEqC5p+QnueRgEqCzJKaKbpMlFJxRUZulu0NMjiMfOZq2JkwVkIECVQeDnWc1WE34ogrLiicwWV\n6WWuyIiawmRBZuZqNmXB2d/ijwGy4BYctmMzyQiUuRpxsNsoIKZmriaXcUVQUkRohkXjGGbd0A4A\nn7z6cXzy6sfH9n6+LJiOuZJFng7+ZgGVBXOY2vUcOVeapcGwTei2gXKMuTvx9VIa2g0y+ibR0O4e\nE/FtFgWm4iqDST8OMyULCgoMy4DjOEOdjUUXV1WpgoetXZi2STuss8wVHDckkcdSVcZRQbJgo8Xm\nuZrLghcYJKKANf+JLGTqjMmCgPtZ0+SJkOJqEunsBCQK4MxbzGfd0D4JqFK2nKu0hWxVqqAilnMy\nV+lzroKyIO2cy8FcyXI6Qzsd2swgCwL+7MOiwJJIT7sFC4pimClZUJDhwAll7TVbL8TMThBmas8y\nV3ASWF9UcdrUUs3UjAKr50oU5gntFxbE28EqDZJ273HkExUNiRdThYhOi+cKcPNXgIvBXI0blLlK\n5bkyU5nZAdfXt1XZxFHvJPNOVLM08ByfKldNEWRw4NAze0zm7sTXy8xcxec9TVIWVETSLVgMc0Vk\nwdlgrqKzrgzLSFXIJyFseDNlrqTpZa4Ad0yNZTtotNk9yFFotN2A2Yoafx1Tz1XKLvZJY15cMYAO\nb2Y0tc80c5XaczV5WZBkXZHFPO0Nfw6/IE3lucrAXAHAdnkDtmPjsHOU+rmAewOU+XTBlG67veIy\nVzkzroAMsiBlruK/LyILdoqWBb1NEFsUQ3GyYLUkJbJ10wAlorhyHAeapdNJHUUgbHjzud5CVaoU\nZpofFWjWVQG+q7OWjqVK8nXsy4Jz5urCIe18wZ7ZA8/xhcyiGjdItyBrK/gkhzYTlFR3QSIyxEUw\ntI8bKmWu2G6stuO4afgZOjPzDnDWLD3TtaWKCjRTy53ODqSXBU1SXCV5rrxj6hYtCzJ5rgqWBVva\nTEiCQLC46u/SNG0TDpxC13J/BE6wuGpOtd+KgGZd5fRd2Y6D87aO5QS/FTDPubrQIJQxO3PVQ0lQ\np3rkQxQkXoIDB5bDdtOYJlnwdC4LZoaSMkRUyxAgSpB3gLOesbgaZK4qOQztmWVBMX5NKI9QFhQ5\ngXZehYGOvymAuepqJjTdmqHiKny+IM24KrhbEPBZf8M20TW7U++3AooLEm11DVi2k5jODmSfeTtp\nzIsrBmRhrmaxUxAAJCEdBevLglNgaG/Ni6usSCsLZklnJ/A7BvdTPxdw2YVMzBUproowtIs8OGSQ\nBRNDREcjC3aNLkpSKXbD54+/yc9czVKnIOBbPwZlQTr6poAAUYJB5qo5A52CBEXJgqT5KKlTEJjL\nghcaRG9n7aLreOnPs4i0FGzLaEPmpYlKoOUB5mreLZgexG/DamjPks5OsKquQOTFTLKg4zjQLQNy\niowrAlVUYNomneOWx3PFcRxkWWCXBS3GbkHCXBlszNW3jr5DO83i4IamxjN1SoEhoqczlHEF+MxV\nZHE1Es+VuzGdlU5BoLiUdmKIX04IEAVmN+dqXlwxII2h3bIt6JaO0owNbSaQ6XxBRuZKb6M64R0X\nYa7IBUtiBeZghyjwEAV+LMwVz/HYKm9gv30A20nHkhi2kdkDQ4JET3tnAPJ386qSUDhzRT1XDLLg\nveYD/PI3fw2fvfW52Mc5jsOUSF/kbMGzGYphAALWj4HiiqS2j9JzNQujbwjKqoSSIuRnrmjGVfL5\n4W/458zVhUMaWXCWA0QB0PZ2ll2C4zhoGq2JmtkBv7giHvy5LJgNqiww51wRb1Yp46ihrfIGdNvA\nmdZI9TyacZXFc+XNFzzpnQJArhBRwGV6WIsrVuZKERRw4Jg8V7dO7wAAvnn0ndhiTLM02I6dWEwW\nOVtw1mTBKEO7P2ppFDlXLnM1S8UV4Pqu8o7AYU1nB+YhohcaaQztJONq5pkrhhNZszSYtjlxr8Dg\nDX4uC2aDIgkpZMHszBWQ3dSu5ZBpSDTKCWGucl6jcgbmKimhned4lESVqbi6fXYXgGv0/cbBtyMf\n1zbYoiekIourmZMFw5mrUciCqqBA4AQ6Ouxcm/65gkGsLqroahY6vexMkl9csXuu5ob2C4g0zBVZ\nFGefuUr+rE198unsgO+5IpgXV9mgygJzt2Du4ipjHEOWuYIEqsdcnWpn4MDRf2eFKgvQdJsptoQ1\noR1wi6BugufKdmzcPnuXsiBf23st8rE0hiGBueI4DrLEQysgimFWZUFtYI0fRXHFcZw3X9DzXBkz\nxlxRU3v24eJn7TSy4NzQfmGRxtDuM1ezWVz5o36SP2vLmI4dV0npv8GX5sVVJihpZMEchnYA2KLM\nVbqOQT2PLOgVZLZjoySqTHNCY19P4mE7DkyLobhinC0IuEVQEnP1qLWHrtnFC2vP4vrSNbx9dpd6\nyQaRJpFeFoXCZEGW9O1pgW9oH5AFaRRDcbIg0D+8+Vwj3YKzUVytF5B11Wjp4DkOC+Xk75XjuNQz\nb6cB8+KKAZS5YpAFO7NeXHm7BBaWrjUFcwUBV87ivRZznuOYbmBzDEOVBZiWTf1Bceh6zNVgYcuK\nzfIGOHCpBzjn8VypAbYrryQIpBt0bKZgrkpiCYZtxN5Mbp+9AwC4sXwNH9p+Pxw4+Nr+N0If22GU\nBQG3YCwkiqGlYWUhXYr+JKFEWD+yzLFkQVWqoGN2YdkWzvUWOHBYkCe7jrJitYCsq7OWhsWKRNft\nJKQdyzYNmN+FGEC7BVkM7TPuuSJtrywnMmkhnnS3IMdx9CavysLMLOjThjTBmHmZK1mQsKquYL+d\nVRbMltBOEDfAmPn1UqS0G4yGdsAvguJM6sRvdWP5Sby8+QJETsBX914LlSipVYHhM8uSkDuKwbJt\nnLd1rMyImR3w58dqZrjnqsjxN4Bvau+YXTT1JqpSJTeTOi7kzbpyHMcdfZPi/Eg7lm0aMBt/zQnD\nN7SzFFez7bmizBWLLOh5rhYmzFwBvql97rfKjjTFQk/L57kCXN9V02j1jQFJQj5De7HMVZpilNXQ\nDvgbs6isK8dxcPvsHSwrS1gvraIslfH8+jPYbe/jYWt36PF+Ij2rLJiPuWq0dDgOsDwjfisAUESv\nuLIHiyt3HSw6xy/YMXiut7CozIYkCOQfgdPRTJiWzZRxRSDxIoz54OaLB9/QniwL/qVirqbEcwX4\nxdU8hiE7FI+FYpkvmNfQDrhxDACwn0Ia9GXB9DdupY+5Gq8smMrQnpB1td85RNNo4cbyNcrSvrr9\nfgDAV/eHje3+LEUW5oqHbljMs0XDMGudgoDPXA2Pvyne0A74WVenWgM9q4cFafJrKCuWqjIEnssc\nx3BGOgVTnB/inLm6mPhLxVzRwNQ03YKTXxjmxVV+qDSlffSyIBAYg5MijiFP9xaJYgCKYa7SMH00\n54rF0E6Yq4jiypcEr9GfPbf2NEpiCV/fe30omJVlaDOBLAlwAsebBbRTcJZkQTr+pt/Qro1g/A3g\nM1fk3J8l5ornOKwuKjjKWFw1SIBoSuZq7rm6gJD+UjFX7Jkifrfg5GVBEsdApJo50kOhxULy3z6v\noR0IdAymYq6mx3NFh10XzFwlyYLEzH5z+Un6M4kX8f7NF9HQz2m4KEHHk11Z2DoSJKrlkAZpgOgM\nMVc8x0PmpaE13hhBFAPgNwHttvcATAf7nwZriyoaLZ2e12mQJkCUQJp3C15M+Ib2NCGiM8pc0fE3\nDMWV3oLMS4UvPFngG9pno/V7GpGmWOjpJgQ+X2cmzbpKxVxl98CMqluQJbqANaEdiJcFHcfB22d3\nUZUqtDgl+CCRBgcyr9IwV0qKzxSFWZQFAbeAGpwt6Hv8RsNc7XpRJLOScUVAfFenzfTslZ9xxX4N\ni7wIy7FSj8uaJObFFQN4jofEi0xS2axHMaQKETXaU7PjmsuC+ZHK0K5buTszq1IFVamCvRRBonlC\nRJWCi6tU3YIpDO1xsuBx7xRnWqPPb0Xw5NLjWFNX8Prht/o2gh2zC5ET6MYpDrLkpbTnCBKdRVkQ\ncM+PocHN9qgM7YS5ms3iajWHqT0bc8XuBZ4WzIsrRsi8zBgi2oXMSxD42bzJy4xDMh3HQUtvTYXf\nCph3CxYB8t0xMVeaVUghu1XexHH3hHlQeK6cq4AsWCrA0J6G6SNBo3llwWAEwyB4jserWy9Ds3R8\n8+g79Oddo4uSVGIqhGWxAOZqBmVBwD2nIsffFJ5z5TJXxEYya8VVnpT2s4yeK2C25gsmaii1Wq0M\n4NcAbAFQAfwP9Xr9dwO//wSA/xGABaAO4Gfr9bpdq9V+CcD3AHAA/IN6vf614g9/fJCEYT0+DB2z\nN7N+K4B9cHPP0mA6FqpT4LcC5p6rIpCOuTILGcq7XdnEncY7OOge4XJ1J/HxeaIYqK/GNsYuCxpe\ndhRrQjsQLgu+HVNcAcCr2y/ji+/9Ab629xo+sPUSAJe5qjDGpdDPlIO5Om3pWChLMxfmq4TIgrql\nQ+CEwjfLg3+PaVEAWJEn6+qspYMDsJimuBLYNv3TBJaz/0cAfL1er38cwE8B+F8Gfv8rAH6yXq9/\nBMACgB+s1WofB3CzXq9/GMDfBvCvCjzmiUAWpERZULd0nPbOsKwsjemoiofEyFz5GVfTsSjMZcH8\nIN2CScWV4ziuLJjDzE6w7cUxsHYM5pEFAT+OoVJkiGiqnKtk9ihOFrx99g5KoorL1e3Q525XtnB1\n4TK+c3ILTb0Fx3HQMbvMxSSVBTMyV47j4KypzZwkCLgFu+VYfdKTbhsj8ZSWRBUc/HNh5pirXLKg\nhmrK4ptu+mco6yrx09Xr9X9Tr9f/hffPKwAeDDzklXq9Tn52CGANwPcD+Jz3/O8CWKnVaovFHPJk\nIPNyYkL7O417sByrr0V61iAJ5CSO/6wk42pamCu/uJob2rOC5lwl3FhNy4ZlO4V811uVLQDsHYO6\npYMDR2WCtCCm9iJDRFmiKwzLgSTyTNKcX1z137jOtAaOuse4vvREbJr3q9vvh+3Y+Iv9N6BZGmzH\nZs718mXBbMxVV7OgGdbMSYJAIHInwF5pll54DAPgsqikwA/+/6xgbdH9+2Zirtp6atabddM/TWAu\nHWu12lcA/AaAfxj8eb1eP/d+vwPgBwD8WwDbcAstgkPvZzMLl7mKlwUJZX9zJZyynwWwGgdb+vQE\niALAM4+v4EPPbuGlm+uTPpSZhcIoC3YLCBAlIMwVa8egZulQhOwz61Sv0aSYEFF2lscwbeadusAL\nkAUZ3YHken+eYPz68srmS+DA4av7r6XqFATc2YIAMo/AmdVOQcD38QWlQcM730YBUlAtzNDoGwJJ\nFLBYkVMXVz3dhKZbqToFgXQRQdMC5u1fvV7/3lqt9hKAX6/Vau+r1+s0wrdWq20C+DyAX6jX68e1\nWm3w6Ykr4cpKGaLIvlhvbIyXRq2oJdgNGytrZYgR+vt733oPHDh88PrzE2d0sn4/S6a7KHKiE/8a\n5+5JvrO6Nva/RRg2NoB/+rfXGB87+eOdRjjk+uO52O/IOnYl4eVFNfd3ueZUIH9VwpF+zPRaFkyU\npOzv+9jyNppGE49tr2e6oQXflyfMHc8nHo8Dt3hlPe6qXEbP0foe/+C9+wCAV689j4216NfZwAJe\n3H4Gb+x9B6c4AgCsLSwxvffaqrtuKaqU6Tt2ePc7vby1OHPX2VLF3ShWlyRsLLrHrjsGVhS2744F\nwddZLi9iv3OIlXJxrz9ObK2W8c6jc6ytVcHzbJudR4fupnx7rTr0meO+g8VdtxCtLMoz812xGNpf\nAXBQr9fv1+v112u1mghgA8CB9/tFAF8A8E/q9fqXvKc9Qj9TdQnA8NCrAE5P2eeLbWws4PCwyfz4\nQmC5i8aj/eNQw7phGbh1/A4uVbfRbdjoYszHF0Ce74fkiLR7vdjX2D05BgA4PWH8f4scmMi5MyNo\nd13K/ew8/m//cN/7ne0U8l1uljbw6HwP+weNxIKno3chC3Lm9/2Jaz8G/aqB46N26ucOnjudnvt9\nnTe1xOPpaSYEDszHrfIqTrVG3+O/tXcLsiBjwVxJfJ2XVl/EG3vfwe985/cBAJzJdp1q3mc6Oumk\n/o43Nhbw7oMzAIDMs3/WaYFjuAXC7uEpJK0Cx3GgmTp4p5g1bvD8keFJ1Hxl5r4rAFgqSzAtG3ff\nO2Yewnz33ikAQBG5vs+ctC4bPfe+dHjSwNoE762DiCv0WLZuHwPwjwCgVqttAagC3nbIxb8E8Ev1\nev2LgZ99CcBPes95P4BH9Xp9er6RDPDnC4Zrvu+e34dpm32pybMInuMhMqThtgzP0D4lsuAc+cFq\n0O4VkM4exHZlE4Zt4qR3lvhYVxbMLjmVRBVLBY0aSTVb0LJTMfMlsYSe2aObnabewl57H08uPs7U\nufa+jechCzKVEpkN7eJfXllQHpAFSWhl0TEMBFQWnNE1lGRdpRmD02inz7gCZjPnikUW/GUA/7pW\nq/0JgBKAXwTwmVqt1gDw7wB8BsDNWq32s97jf6Ner/9KrVb7C8+nZXvPmWn4Zsfw4orkz8x6cQWQ\nUQMJhnbPczUtOVdz5Ico8BAFLtFzVcRcwSC2yRic9j7WS6uRj3McxzUYT8FEACDwfbHkXJk2pDK7\nDFmWVDhw0DN7KEtl3GH0WxEogoz3rT+Pr3mDnEuMhmk/XiKboX1WA0QBQOFJceV+Bj1HphoLyAic\nWesUJCBxDCfnGq5fYnsOGdqcJuMKCBraL1BxVa/XuwB+OuYhoVdRvV7/x1kPahpBR+BEmNqT8mdm\nCSJDceUzV9PRLThHMVBlMbFbsFegoR0AtsgYnM4hnsczkY8zbBMOnJHd7LJAkQS2hHbLhiSym/DL\nolsMdbziyjezs3cif3D7ZVpcpWauMkYxzGqAKADIoreB9tY+wmBJBY++ISDM1eKMMlfrGeIYSIBo\neuaKfXKIZun41uGbeN/mC5m7iovAbLUoTBCEGg5jrkzbxN3Ge9ipbE3cyF4EZF5KzBNp6i3Igjw1\nLMIcxaCkigzMVbHFlc9cxXcMjppJyAJFFhKZK8dxXOYqRa4PGZ/VMV0v6u2zuxB5EU8sXmF+jdrK\nDSo5sedc5WOuTlsaJJFHRZ29SBSfuXLPs1GNviHY8WJILjGE504jsozAadDiKt13mmYs21f3/gK/\n+p3fHBpgPm7MiytG+Amxw8zVveYDGLZxISRBABB5iYm5WmBMfZ5jdqDKYmJuU1crVhbcKK+DA4f9\nhKyrvAGio4AiJRdXlu3AASAyjL4hIMVQ1+ihY3TxoLWLJxavpGJRBF7ARy99CCInYKPM1klL4iW0\njJ4rEiCaZ+bkpEACZgdlwVF5rp5fewb//Yf/MZ5evTmS1x81sqS0U1kwbRSDwC4LHnbcZqvyhOf7\nzt72YkKIM7TfPk3nh5h2yLyIRsxJTOYKXl5gFNrnmBmUFAF7rIb2gpgriRexXlpNDBLNM1dwVJAl\nAVrCzp2ks6dhrsoSkQW7uNt4Fw6cTOvLD137FP7KYx9lZtTzzBY0LRvnbR03ryynfu40wF/jPebK\nW+tHxc5zHIe1GI/htKOiilBkIVVx1WjrqKgipBTNHUC6nKtTzW2MWVEnex7OmStGUEN7CKNzkfxW\ngMtcxY366Vk9mI41Z64uIFRZhGHasOxoWYga2pXi9mZb5U20jQ4dqxSGPHMFRwVVEqCbNmzHiXyM\nYXnFVQrmKigLEr9VFmac5/hUVgUaIppBFjw91+BgNjsFgeEQUX0Kz7dpAsdxWAu4EUcAACAASURB\nVFtUcZKmuGppzLENQaQZf3PaO4PACRNvFJgXV4wY3NUQWLaFO413sFXeKKzFe9KQBAm2Y8Oyw3ev\nTe8GWJ1RI+Yc0SBjhDQ9rrgq1nMFAI95LOhrB9+MfIwvC07PzY6k2scxPSaZK5hFFjR7uH12FzzH\n49rS4zmOlA3+4Ob0zNXxuZsGP4udgoAvN5M1XrNJcTUaQ/tFwNqiinbPpFaBOBimhXbPTN0pCASj\nGJI9VyfefN9Jp97PiytG0G7BAVnwQesRNEu/MKwVEOzMCL9gaKfgPIbhwoH4qAg7FQa/uCqOufrY\n5e+FIsj4t+/8Hrpm+E54GpkEmWHYNWWuMsiCZ70G3ms+wNWFx8ZSVAo8B57jMjFXxNg8i52CwHDO\nFW2gGJHn6iIgzYzBRotkXGUpruLvSQSmbeJcb2JFXUr9HkVjXlwxwpcF+5mrty9QvhVBkr5NM64u\nQGfkHP1QleRgTN/QXhxztaQs4FNXP4Gm0cLvvfdHoY/xPVfTc/NWGYJEjQzMFZEF3zx+C7Zjj219\n4TgOssRn8lwdNzzmakaLK18W7De0jyqK4SLAz7pKLq7e22/1PScNWAc3N7RzOHCwoqykfo+iMS+u\nGEH/uAPM1dunsz+seRBJJ3LL8IY2z5mrCwciC8Z1DJLfKQUWVwDw/Ve/D8vKEv7g/h/jNCStfSqj\nGLziKu77ymRo92TBg647DCNNvlVeyJIAzUzPXJ14zNXsyoL9cTujjmK4CFhLEcfwZ99yJ+B9oLaZ\n+n1ERuaKTHlYnbCZHZgXV8wIM7Tbjo07jXewXlrDsjJ5GrIoJAW2+Z6rOXN10UCkvjiZq6ebUGQB\nfMHt9rIg40ee/GswbBO/c/eLQ7+fRs+VLCcbwM1MhnY/l4oDhyeXnsh2gBkgi9mYq6MzIgtOz98n\nDeQI5mqaZOhpA+sInLOWhm/eOcbj2wu4upXem8waIjotnYLAvLhiRpih/WFrF12zd6EkQSA5U2TO\nXF1cUOYq5uba061CJcEgPrj9fjxWvYSv7r2Ge+cP+n4367JgGuZKEWRqyH2suoOyxBYCWgQUScgm\nC3qG9rTp29MCkRchcIJvaB9xztVFwHpgBE4cvvLtPdiOg4+9mC0wlXW2IGGuVqaA7JgXV4wIM7Rf\nRL8VkJyG25ozVxcWZBhzPHNlFWpmD4LnePz4jR8GAPy/t38XTiDiYBqjGFhkwSzMFcdxVBocd7OM\nLPHQM8iCx40eFssSxBRF5LRB/v/bu/MoSa7qzuPfyMzIzKqu6kVN05KQENp4IBDLILEYgcR6wBrE\nMUaDWcyAPWMfDNgM+/iwrzbCGmYAD3CQwTYWY5gZGDYbDIOwxCrjASGWZ1YZoa3V6qWqa8kt5o8X\nERmVFblUd1ZmROTvc44OXdlZXZFBRNbNe++7r1yNr7PmNs+5KoLdCzVKnjewLBgEAdfecCt+pcTD\nztt/XD9n1LJglLk6qa6eq9xIa2j/yaFizbeKVOP+siEN7cpcFU40u2pQJmZtvbVtmSsAc9I53H/v\nffnx4Z/xvTt/ED+eySGiI4xiiBvatxh0dIOryfVbgRsk2hwyu6tXEAQcPLKW25WCkVoiuOpeb2po\n76dU8tizWBu4WvDHNx/h9rtWeIjZx3z9+M7lqKsFo15NlQVzJEoNR59mOkGHnxz+OSfV97B3bvpR\n8jhVhja0H6NWrmr+SwF1RzGkBwvtTodGqzO26ez9/MY5v07JK/HJn34unreWxYb2LZUFt5C5gu7G\nvmdPOrgKX1NzC+MYVtZbNJrt3DazR2rlandCeyd7mdIs2rurzuHl9ThD2+vaG24B4FHnH/8eiuVS\nmZJXGjrn6tDaYerlerzadpoUXI0oCiSiTzO3HrudY62VwpUEYfinhKXGsrJWBdUtC6b/f7++DTOu\n0py8Yz+PPPVh3L5ygOtu+ab72RndWxCGrBY8jrIgwGVnP4nn3OfyePPlSTme/QUPLbn/b/I6hiHi\nyoJRQ3tYFlTP1UB7d9YIgu41kLS63uL6H93B3XbVMWecWBLCL1VGWi2YhZWCoOBqZGXPRc7RasHu\nljeT/VQ5CYNGMQRB4DZt1nT2QqoPGcUQDxCtbW/mCuDSM59AvVwLB4uuJiZmZ+eX3ShlwdZxlgXv\nveccHnHqhcd/cMfpePYXPBz+Yi1CWbDRadIJOonVgsrQDzJo1tX1P7qDRrPDRQ845YRXF/slf9Mo\npKTV1ipr7bVMlARBwdXIPM+jWvJphjdcUfutYHDmaq29Rjtos6B9BQtprjp4teDqhDJXAIvVBZ54\nxmNYbh7j87/4MuutBh5efH1mwUhlwbbrXdpq5mpajmd/wThzlfOyYBS4NzstGp0GJa8UN1NLumjW\n1Z0pTe3X3nALHnDRCZQEI5UhmatDa0eAbPRbgYKrLfHLbkPjIAj4yeGfs6u6k31ze6d9WGMXjWJI\nq29rOnuxxQ3t/TJX2zCdfZDHnP4o9tR28+Wbr+PO1YNUy/7U9wxLisuCA3uu3N/lZRXd8ewveGi5\nGGXBqOS83l5nvd3IVH9fVkXBVW/m6pY7j/HTXx3lfmeeFM/DOhF+qTKw5+qutUMA7KkpuMqdaqlK\no93k9pU7WGouc+6es/DGPEgxC+LMVUoKVvsKFlsUNPUNrsLHt7uhPVIt+1x29pNodVocaRzNVEkQ\nEhs3jzKhPSeZq+pxZK4KUxYsRVPaGzTajXjltPQXlQV7Vwxed4ObyP6oB546lp/jl/zBmav17Exn\nBwVXW1It+zQ6jUS/VfFKgpDsudp8IStzVWzz9cFlwWhD50mUBSMX7H8Q91w8DchWMzt0M1eDyoKt\nnJUFj6fnqkgN7eAWLjXazcwF81l00uLmLXBa7Q5fu/FWFuZ8HnTO3cbyc0YuCypzlT9R5ireT7Cw\nwVX/IaLRAFFlroqpUi5RLnlDM1eTKgtCNFj0UiBbYxhg1LLg1ie0T1M1Dhi3kLk61qBaKTFfy3d/\nUi0ZXHUaCq5GUKuWWZjzOZiY0n7DTw9ydKXJw++3f2wfKlxZsLVhsHBSlvYVBMj3nTBh1bJPs9Pk\nJ4d/xmJ1gf3z+6Z9SNuiMihz1Yymsyu4KiLP86j55RFWC072rePcPWfz9HMvy0yzasT3S3gMLgtG\n838qeclcRWXBLfRcLa802LVYy32bRHfz5jBzpTEMI9m7q86tdx4jCAI8z+Pa74azrR4wnpIguIpK\nQEA7aFPxNr//HFo/hIeXmX1+FVxtQdTofaSxxIPv/oDcv5H0Uy33H8Ww3Ij2FVRZsKhq1TLrzfT0\n++qEG9qTHnP6RRP/mcOUPI+qXx6Y5elmrvLxflE7jrLg0kqTe5689Q15syYKrlZaq7SDduYypVm1\nd2edm25bYmm1SbsdcMPPDnKvkxc5/e7j+xDul7sVlbQVnIfWjrCzuki5NPn3pjT5+CiVEclPMUUt\nCUJiH6eU7W+Wok2blbkqrHq1PEJDuz6XRWp+aXBZMB4imo03/WG22tC+3mjTaHXYmfMxDNDtuYp6\nS33NuBpJtGLw4JE1vnbjrQQBPOo4N2nuZ1BFpRN0OLx+JDMlQVBwtSXJYXJFDq6qAy7ieNNmZa4K\na3BZcHqZq6yqVcsjDRHNTUP7FkcxLK242X+7duQ/y9MbXClzNZp4xeCRNa47wU2a+/EHfOg/2lii\nHbQz1TaQj7s9I6KgY4c/z8k77j7lo9k+lQEN7UvNZerlmj7RFVi9WqbR6tDpbG4cnUZDe9bV/P6Z\nPuhmrio5KQtWK1vLXC2tuveJXQXIXEWrUaORM+q5Gs3ene68feMHt3P7odUT2qS5nyi4Spt1FW/Y\nnJGVgqDgakuiTzXn7D4rU4MMxy0axZB2ES83jilrVXCDxgvEPVc5XxU2TjW/vC0bN0/L8WaudhYg\nc1XryVxp65vRRJmrf/6XA8B4G9kjg8qCh9azNZ0dFFxtSZS5KnJJELqfEBo9F3G0r6BWChZbNBgz\nrTSozNVmtWqZdieIVwX2arY7eB6US/l4u42Dq1EzVytFylz1Blf5DxgnITmBfd/uOuae4w9yBm3L\nFk1nV89VTp2z+yz2z+/jgfvuN+1D2Vblktukujdztdpy+wouaoBooUUDQtOyMWuNNpVyKTdbuUxC\nPOuqT2mw2erkJmsFUIvLgqNmrsLgqgCZq7jnqrnkvlZZcCSLc35cTr7o/BPfpDnNSGXBDAVXyu1v\nwX333pvX7X3FtA9jIvxSZdP2N9FKwQUNEC20QVvgrDVaylr1qPmJ0QVzm8tIrXYnNwNEITFEtDVq\n5ipsaC9U5upY+LXKgqPwPI+9u+rcdnCFR45hk+Y0UbtKb0UFstlzpeBKUqXt4xRPZ1dZsNC6mZjN\nb2JrjbaCqx5RGbVf31Wz1cnNAFFIjmLYWuZq50IV+kzPzouooX21tQqoLLgVz3z8uRw91hjLJs1p\nBmau1g/jlyqZ6gdWcCWpUoOrOHOVnQtYxq8+IFhYa7TYu3Nu0oeUacPKgrnLXG1xiGh3FEONleW1\nIc/Ott5gSsHV6O5/5t5t/fcHNbTftXaYPfXdmRrsnZ87XibKL1U2jWKIN21WcFVo/RragyBgrdFm\nrqbMVVLVHxyM5K3nqlTyqJQ9GiOWBY+uNCmXvHjT7zzzSxU8ur+gFVxlR7+G9ka7yXLzGCfV9kzj\nsPrKzx0vE+WX0zJXKgvOgngUQ09w1Wh2CIJuw7s4gzJ9EAZXOcpcgctebSVztTjvZyprcLxKXmnD\nDL+aGtozo19Z8NC667faXc/GnoKRfN3xMjGVQZkrrRYstHqfzJWms6cbqSyYo8wVuL6rrQwRXZwv\nThCSDKg0LDk7KuX0smDUzH5ShprZQcGV9OGXKrQ6LYJEg2qcudJqwUKLy4LN3uBKM67SRA3gaZmr\nThDQage5G11R9cusjzBEtNlqs95oszhfnCAkueWNtr/Jjjhz1U4PrvbUVRaUHPBTmgfjfQVVFiy0\nuh/OuerJxKzGmSuVBZOi85GW6Wm38zWdPeLKgsMzV9FKwSJlrpJ9VtHgaJm+7u+kjRWVu8KyYJYG\niIKCK+kjbQuceF/Bkn65Flm/OVdr6+5rNbRvVAszV2mjK6Ktb/KWuar5pZF6ruLgKmW+V15F4xhA\nDe1Z0q+hvTvjSj1XkgNpF/JyY1lZqxnQLQtufBPrlgUVXCd192LcnOnJ276Ckao/eEufSDSGQWVB\n2W6VPpmrLE5nBwVX0kdvCjYIApaax1jUGIbCq/XLXKmhPVV0vtIyPc3clgXd8TaHjGOIM1cF2Pom\nksxW+QquMqNv5mr9MAv+jsxlGfN1x8vE+D0rM1Zbq3SCjjJXM6Duq6F9KwatFsxrWXDY7K5InLma\ny9YvthMRZatKXomKp2s9K7qjGLrBVRAE8QDRrMnXHS8TE39KCPcXXIpXCipzVXR+pYTnqaF9VLUB\ngUir7Vbb5i5zFa2AHJa5Wo0a2otTFowyINVSMWZ3FUVaWfBYc4Vmp5mpPQUj+brjZWIqPSnY7owr\nZa6KzvM86tWyGtpHNGhvwbjnquiZqwIFV1HmKmtlplmXVhaMBogqcyW5Ue35lLCszNVMqfnllCGi\namhPM7gs6B7L08bNALV4f8ERe64KNIpBwVU2Ra0qybLgXWvZHMMACq6kj27mKgyulLmaKbVqJaXn\nSg3taSrlEuWSV8iy4PDMVZOSV4x9BSPJsqBkR9T/1mh3y4LdMQwKriQnehvalxqazj5L6n5KWVAN\n7X3V/HIxy4JDprQvrTRYmPcpFag3KZpzpTEM2eJ5XrxzSOSu9UOAyoKSI70N7ctN7Ss4S+pVFyx0\nEtsfqaG9v1p1cxkViOdE5S5zVYkyV8PLgkXqt4JE5krBVeZUSv6GhvbDa0cAlQUlR7oT2nsa2tVz\nNRPSZjetNdp4XrdkJF01v5w+5yoexZCvzE7V79+kH2m1O6ystwo1nR2SPVfFel1FsClztXaYkldi\nZ3VxikeVTu+SkioKrho9De3quZoNaVvgrK23qVcrWp6eouaXN/WoQY6HiEY9VwNGMSyvFq+ZHRLB\nValYr6sI/FJl02rB3bVdlLzs3V/ZOyLJhN6BbcvNY9TLde0rOCNqKYNE1xot9Vv14fbi62woo0Jy\n+5t8nbfuasH+mavuSsFiZXiioEplwexJlgXbnTZH1o9mspkdFFxJH72Zq6XGMovqt5oZaVvgrDXa\nCq76qIV9aM2eHqW8lwUHB1fRjKtiBSHz/hwAc5X6lI9EeiXLgofXjxIQZLLfChRcSR9+uZu56gQd\nlpvHWNBKwZkRBVFrjd7MlTKXaWphGa23NJjbhvYRyoJHCzhAFODUHSdz+b2fysWnPXLahyI9/JIf\nlwWzPEAUQO+Ukiq5cfNqay3cV1CZq1nROxiz1e7Qageazt5HrU8DeG5HMVSGN7QXcYAouCX/lyiw\nyiS/VKEdtOkEHe5aC8cwqCwoeZIcxRANENWMq9kRZaiiX66azj5YvLqyZxxD1NCetwnt3SGi/TNX\ncXBVsNWCkl2VcncLnEMZns4OCq6kj27mqhVv2qzM1ezoZq5cCn5tXdPZB0lbAADQymnmqjbCENHl\ngpYFJbuSFZVD627GVVbLgvm642VikjuQx5krjWGYGb2jGFY1nX2gvmXB3PZcudfT26CfVNSyoGRX\nchX7obAsqMyV5Eo1kX6NM1caIDoz4uAqLgtqOvsg/cqCec1cRRPa1wdkrpZWGnjAgsqCMiHxnrft\nFnetHaZerjFXmZvyUaXL1x0vExNnrtRzNZNqPasFo/9VQ3u6fmXBvPZc+SNsf7O02mTHnE+plK8x\nE5JfG8uChzNbEgQFV9JHxSvj4fX0XCm4mhVxmauhhvZRDF0tmLPgyvM8qn5p6BBR9VvJJEVlweXm\nMqutNQVXkj+e51EJB7Z1e65UFpwV8ZyrqCyohvaBhq0WzFtZENw4hn5zrjqdgGOrTfVbyURFmavb\nV+4E4KSMjmEABVcygNvHqameqxkUTRxXQ/tohq0WzFtZEKItfdIzV8urTQK0UlAmK8pc3bFyAMju\nSkFQcCUD+OE+TsuNZeYq9biZUIqv7vf2XKmhfZBatf9qwXLJo5TDza6rfrlvcFXUrW8k2ypxcOUy\nV1kdIAoKrmSAaAfypeayslYzxvdLeMB6NOcqylypoT1VPBeqsXlvwTxmrcCVBdf7lAU1QFSmISoL\nHlgNy4LKXEke+WWfRrvBseaKZlzNmJLnUa2Wuz1XamgfqLu3YGvD481WJ5f9VkDc0B4Ewaa/W1qN\nZlwpuJLJicqCB1YPArCnvmeahzNQPu96mQi/VGGlter2FdQYhplTr5YTqwVd0DCnnqtUcY9az+iC\nVruTu5WCkapfJgig1U4JrlQWlCmIyoKdoIOHx+7azikfUX/5vOtlIqJZV6CVgrOo7icyV+tqaB+k\nFu/Ft3kUQ24zV9Gsq5RBot3p7MpcyeT45e71trO6kOk+4Hze9TIR1URwpczV7KmlZK5qCq5SVXsW\nAERa7SDXmStIHySqzJVMg58IprJcEgQFVzJA8lOBNm2ePXXfBVdBELDaaFP1S5RLestIUwqHbqYN\nEa3kPXOVsmJQmSuZBj/xgT/LYxhAwZUMkEzBauub2VOrVgiARqvDWqOtZvYhan430xdptvLdcwWb\nx0tAN3OlfQVlkpIf+PfUdk3xSIbL510vE+ErczXT4tlNjTZrjZb6rYao+eUNgUi706ET5LksGPVc\npZQFV5vM1yq5zcpJPiV/J52ksqDkVTIFq8zV7OkOEm2xtt5WcDVErbpx6Gar5VbZ5TUAqVWinquU\nzNWxhkqCMnEqC0ohKHM126LM1ep6m/VmmzmVBQeq+eUNDe3xvoK5zVylN7R3goDl1Zaa2WXikmXB\nLO8rCAquZAB/w2pBBVezJspUHTnW2PC1pKv5ZdqdgFYYVDWjfQXL+dv6BpJlwY2Zq5W1Fp0gUOZK\nJm7jakEFV5JT0YU8V5nL9DwR2R5xcLW87r6u6RoYJN4CJyyjtfKeuaqkN7R3xzAouJLJihZZVUqV\nzH/gz+ddLxMRZa4WM34Ry/aIgoXDUXClzNVAURk1Kg1GmSu/ks/zFmeuesqC3TEMKgvKZEW/k06q\n7cbL+GboCq6kr+hTwoL2FZxJUbBwWGXBkURT2qNMT/7LgmEmrtUvc6XgSibLL1XY4c9z6sIp0z6U\noZTnl76iUqAyV7Mpmmt1ZNn9MlVD+2A1352fKNOT97JgrTIsc6WyoExWySvxqgv+iHqlNu1DGUrv\nltJXtP2NVgrOpqgseERlwZHUqi4YibYKisuCOR3FUPXTRzGo50qmae9ctudbRfJ518tEdDNXKgvO\noiiYOhxmrtTQPlgtnmhejMxVv1EMceZqTmVBkX7yedfLROyb3wvAPRZPnfKRyDTEmatjylyNone1\nYP4zV2EPWW/P1arKgiLD6KOo9HXPxdN4+0WvVeZqRkXBVKsdbPha0tX8ntWCYeaqktfMVZ8J7Wpo\nFxlOwZUMtLO6OO1DkCmp9QRT2rh5sHgvxoJkrmoDRjHUq+XcljtFJkF3h4ik6s1UKXM12KayYN4z\nVwNGMagkKDJYPu96Edl20S/XiBraB+stC7ZynrkqlzxKnrchcxUEAUsrTZUERYbI510vItuu5Hlx\nwADKXA2zqSyY89WCnudR9Usbeq5W19u0OwGLc8pciQwy9KOoMWYe+DCwH6gDb7bWfibx93Xg/cD9\nrLUXhI9dAnwc+H74tO9Za1881iMXkW1Xq5bjYGFOwdVA/VYL5rUsCC57ud7qZq6WVtXMLjKKUfL8\nTwH+yVr7DmPMGcA/AJ9J/P0VwHeA+/V831estU8fz2GKyDTU/TJHcSWiSk7LW5MSlwV7N27O8Xmr\nVjZmrjSdXWQ0Q4Mra+3fJr48Hbi55yl/DOwFnj3G4xKRDIhKXfVqOfMbpU5bXBbctHFzfoOrml/m\nSLi3JGgMg8ioRr7rjTFfA64GXpJ83Fq71OdbzjPGfMoYc50x5gkncIwiMiXd4ErN7MN0J7RHDe1u\nPliuM1e+Mlcix2Pkd0xr7a8ZYx4EfMQY80BrbTDg6T8G3gh8DDgL+LIx5hxrbaPfN+zZM0+lMnpP\nx759mr80iM5Pfzo3gyXPz84dboPUhXlf543B104QBJRKHp3APa8cBlv7777Ivn35HMS7Y75Ko7XE\n3r0L7rWF2cvTTtmVei50jQym89Nf0c7NKA3tDwHusNb+0lr7HWNMBdgH3NHve6y1vwKicuJPjTG3\nAfcAft7vew4dWhn5oPftW+TAgX4JM9H56U/nZrDe8+PhPkNVyt7Mn7dRrp2aX2Z5pcmBA0ssLa8B\ncPTIKj6DPotmlxce9i23HaHml7ntwDIAnWZr07nQvTWYzk9/eT03gwLCUfLVjwZeBmCM2Q8sAHcO\n+gZjzLONMS8P/3wybqXhr0Y8XhHJiGj8wpzKgiOp+SXWmy0AmuG2QXnuuarGU9pdaTDuudIoBpGB\nRnnHfB9wlTHmWmAOeCHwXGPMEWvtJ4wxH8c1uhtjzDXAB4BPAVcbY54KVIEXDCoJikg21X33FqEZ\nV6Op+WVWe4aI5nmVZXd/Qfdauj1XamgXGWSU1YKrwLMG/P3lff7qKcd7UCKSDWpo35patczhcHVd\n3oeIQmJ/wVaUuWpS9Uub9p0UkY3ye9eLyLZLjmKQ4Wp+mUajTRAE3SGi5fyOsIj3F4wyV6sNFueU\ntRIZRsGViPRVD3+51msKrkZR88sEQKPVodnqUCmXcj0fLOq5Wm+2E/sKqt9KZBgFVyLSV00N7VuS\n3F+w1e7kuiQIiZ6rVpv1Zptmq6N+K5ER5PvOF5FttWuH+0W6a0G/UEcRDxJtuEDEz3FJENz2N+DK\nghogKjI6fRwVkb7OP2svr3zmgznntF3TPpRcSE5pL0TmKrEZtYIrkdEpuBKRvkolj/ucsWfah5Eb\nybJgs9WhXsv3W2w856rV0b6CIluQ749VIiIZUryyYDdYjDNXGiAqMlS+P1aJiGRIkcuCrXBulzJX\nIsMpuBIRGZO4LBhmrvI8nR0SQ0SbnXgoqnquRIZTcCUiMiZRMLKy3iIg39PZIZG5arVZXlVDu8io\n8n3ni4hkSC3cizEKRPycZ67SRzGoLCgyTL7vfBGRDKlV3VtqFFxVipK5ChvaK2VPWyGJjCDfd76I\nSIZEDe3HosxVQYKr9XAUw+J8Ndfb+YhMSr7vfBGRDImCq+XVFkDuG9q7ZcE2S6tNjWEQGVG+73wR\nkQyJVgsuFyZz5Y7/2GqT9UZbzewiI8r3nS8ikiGbyoI5z1yVSyUqZY87j64BamYXGVW+73wRkQzp\nlgWLkbkCN6X96LLb+mZBmSuRkeT/zhcRyYgouFpZdz1Xec9cgSsNBuGfdypzJTKS/N/5IiIZUSp5\nG7JVeR/FAN0Vg6ABoiKjyv+dLyKSIbVEMFKIzFUlGVwpcyUyivzf+SIiGbIhuCpA5ira0geUuRIZ\nVf7vfBGRDKklJpjnfc4V9JYFlbkSGUX+73wRkQwpWuaqWlHmSmSr8n/ni4hkSLKMVqTMVbnkMV+r\nTPloRPIh/3e+iEiGFC5zFQaLC3O+9hUUGVH+73wRkQxJ9lwVI7hyr0clQZHR5f/OFxHJkKKNYqhV\nouBKzewio8r/nS8ikiFFLQsqcyUyuvzf+SIiGbJxFEP+e5TisuCcMlcio1JwJSIyRoXLXFWUuRLZ\nqvzf+SIiGbKxob084Jn5UFNDu8iWaWiJiMgYJTNXRSgLPvje+/jpLUe44D53n/ahiOSGgisRkTEq\nWllwYc7neU++77QPQyRX8n/ni4hkSNH2FhSRrdOdLyIyRlHmyvPcljEiMnsUXImIjFEUXPmVkraL\nEZlRCq5ERMYoKgsWYTq7iBwf3f0iImNUCyeaVwrQzC4ix0d3v4jIGMVlQWWuRGaW7n4RkTGqV7s9\nVyIym3T3i4iMUaVcwvOUuRKZZRoiKiIyRp7ncf5Zezll7/y0D0VEpkTBKwngCAAADDdJREFUlYjI\nmL3k8gdO+xBEZIqUtxYREREZIwVXIiIiImOk4EpERERkjBRciYiIiIyRgisRERGRMVJwJSIiIjJG\nCq5ERERExkjBlYiIiMgYKbgSERERGSMFVyIiIiJjpOBKREREZIwUXImIiIiMkYIrERERkTFScCUi\nIiIyRgquRERERMZIwZWIiIjIGCm4EhERERkjBVciIiIiY6TgSkRERGSMFFyJiIiIjJGCKxEREZEx\nUnAlIiIiMkYKrkRERETGyAuCYNrHICIiIlIYylyJiIiIjJGCKxEREZExUnAlIiIiMkYKrkRERETG\nSMGViIiIyBgpuBIREREZo8qkfpAxZh74MLAfqANvBp4OPAQ4GD7tCmvtZ40xzwZeAnSAD1hrrzLG\n+OH3nwG0gedba39mjHkg8N+BALjBWvuC8Oe9Arg8fPyN1trPGWN2AVcDu4Bl4FnW2ru2/cWPyBgz\nB9yIOzdfAv4aKAO3Ar9trV2f1XMDm87PJejaAcAYcwnwceD74UPfA96Brp9+52YRXTux8HW/EmgB\nrwNuQNdOLOX8XI6uH4wxvwv8duKhC4BHcoKvyxjzeOBtuPP1OWvtm8N/478ADw//jT+y1l5vjDmd\nlGt1m1/6SCaZuXoK8E/W2ouBfwdcGT7+n621l4T/fdYYswN3AT8e9wv0PxljTgKeBRy21l4EvBV4\ne/j978Kd6EcCu4wxTzbGnAn8FnAR8G+BK40xZdyFf034b/xv4FXb/7K35DVAdNO8CXivtfZRwE+A\n35nxcwMbzw/o2kn6SuJcvBhdP0m95wZ07QBgjNkLvJ7uMT8VXTuxPucHdP1grb0qOge4c/SXjOd1\n/TfgN3GB2hONMecZYy4GzrXWPgL43fA5kHKtbvsLH9HEgitr7d9aa98Rfnk6cHOfpz4MuN5ae8Ra\nuwp8FXeSHwd8InzOF4FHGmOqwJnW2uvDxz+Nu7gfA/ydtbZhrT0A3ASc1/NvRM/NBGPMfXDH+Nnw\noUuAT4V/jo51Js8NpJ6fNDN7flJcgq6frZjVc/N44IvW2iVr7a3W2t9D105S2vlJM6vnJ/I64E85\nwddljDkLuMta+0trbQf4XPi8xwGfBLDW/hDYY4zZSfq1mgkTKwtGjDFfA07DRbAvBV5kjHkpcAfw\nIuBk4EDiW+4ATkk+bq3tGGOC8LFDKc89OOzfSDyWFX+Ge/3/Pvx6RyK9mXb8qY8X9NzA5vMDunaS\nzjPGfAo4CXgjun6Ses8N6NqJ3AuYD8/PHuAN6NpJuhebzw/o+okZYy4Efokrm57o60o7j2cDdwO+\nnXj8QPjctGs1Eybe0G6t/TXgMuAjuFrpq621jwW+Q/fCTfL6/FNpj4/juRNnjHku8HVr7c/7PGUr\nr6vf47k8N9D3/Oja6foxLmh4Ki74vIqNH5xm+fpJOzcfRddOxAP2Ak8Dngd8iI3HOMvXDqSfH733\nbPQfcH1lvbbrHGz135iKiQVXxpiHhM1nWGu/g3vz/174Z3CpvfOBW3ARaeQe4WPx42GToIdrYNs7\n6LkDHo8ey4JLgacaY76Bu1BfCyyHDdywhddVwHMD6efH07XjWGt/FZbdA2vtT4HbcGnzmb9++pyb\nf9G1E7sd+Jq1thWenyVgSddOLO386PfWRpcAX8Nlk070dY3yXIBTcecx7fdkJkwyc/Vo4GUAxpj9\nwALw/rDGCu7/oBuBbwIXGmN2G2MWcHXra4Ev4FYbgGuO/7K1tgn8yBhzUfj404C/B/4vcKkxpmqM\nORV30n/Q82/8ZvjcqbPWPsNae6G19uHAB3Gr4b6IO0boHuvMnRvoe35eoGvHMcY82xjz8vDPJ+NW\n5H4IXT/9zs2VunZiXwAea4wphc3bC+i9Jynt/Oj3Vig8zuWwn+qEX5e19hfATmPMvYwxFVz70BfC\n/54e/sx/A9xirV0i/VrNBC8Igon8oDC6vArXzD6HS9Uv45aMr4R/fr619g5jzNOBV+CWXL7bWvs3\n4eqCDwLnAuvA86y1vzTGnAe8HxcoftNa+9Lw570YeHb4b7zGWvul8KL/CC66Pgw8x1p7ZCInYETG\nmDcAvwA+D/wVbmzFTbhz05zlcwMbzs9N6NoBwBiziFvSvBuo4u6t/4eun37nZhVdOzFjzO/jVmAB\nvAW4Hl07sZTzs4SuH8BVpIC3WGufHH59wq/LGPNoXHM8wP+y1r4z/Df+BJek6QAvtNZ+1xhzCinX\n6iRe+zATC65EREREZoEmtIuIiIiMkYIrERERkTFScCUiIiIyRgquRERERMZo4hPaRUREZLYYtz/g\nx4HfsdZ+JuXvm7htgyKPw60k/EvcasAq8FJr7TeNMdcAO4Bj4XNfZq39tjHmcbjdPNrAn1u3efa7\ncbPIAOZxez0+cSvHaYx5APBe3ErFQ7hNplcGvV4FVyIyNcaY5+Bm1bzbWnv5sOcf5884D6hba/95\nO/59ERnMGHM2bru7rw542hHrNoFOft9zgL+21l4dBj1vBqLA6PnW2hsTz60A78PtZXgXbtbfVba7\nWTvGmNfjZmxt9TjfjQvgvmWMuQI3rf/PB7wWBVciMh3hDKDXWWs/QneY4Hb4DdykbQVXIhMSziS8\nxlp7DW6a+tNwsy5HZq29MvHl6cDNA57+EODH1troOc/oOZ49uGzYm8KvXwg8C5eN+qS19s8GHOdT\nrLVHwz/3TqJPpeBKRKblL4AzjDFfAM6z1p5mjPkwcCdwX+B+wKtxk60fAFxnrX0BgDHmbbgp2HPA\nV4BX4jZt/RvcFiNzuGGGPwReDBwxxqzgAqz34zaZ3YkbaPj58BfByeF/D8QNMXwQcAHuDfcy4GLc\nEMmbgDNxgw9/K/GmKyIpohKaMWbQ0+rGmKuBM3DDQ68Mv+dk4NPAIvDYxPPfZIy5G+4efwluk+2G\nMeZjuCnw77HWfjTx/P8IfMhaGxhjzsRNfI+myX/VGPNxa+2/ph1ndI8bY3YAz2WED4NqaBeRaXk9\n7lPg7/U8vt9aeyluQ9z3Ai8EHgo8L9xe5HLgHtbai621DwXOwW2T8QzgR2Fp4WJg3lr7ddyWGFdY\na6/GBU+vtdY+DvhD4K2Jn3tfXJbr+bgywBXAhcD9cQEXuE/Hrww3oD+IKw+ISMgY86KwJ+p5wLuM\nMdeEJb1hXo57L3gi8GxjzAUA1trbrLUX4sp1Hw6f+1+BV1hr44ntuA9V9wx/7mXAn4RbFkWeBfyP\n8M8PxU3N/3L43yIuOBv0unbg9pJ8p7X2h8NejDJXIpI1Ub/DzcAPrbWHAYwxB4FduJ6KR4Rv4ISP\nnQn8HfAHYfbrs7gMVa9bgSuMMW/FNcjeLfF3Xw8/1d4M3B5u1Isx5lfhzwD4vrX2V4njfNAJvlaR\nQrHWvgd4T09ZcJTve1/0Z2PMl4Dzw4DmBmvtIWvt54wxfxU+9xOJb/007oPVt4HrwyzZijHmRuBs\n4KAx5lzgTmvtavg9DeCz1trfH+XYwn6u/wNcba398Cjfo+BKRLKm1efP4D6drgMfiPYcSwqb1y/G\npe1fgisdJr0H+Ki19i+MMfcHkquWhv1c2Jjt93B7pYnICTCuDvd63P6DZdx9+z/Drx+My4CdD/zS\nGOMB/wA8PfzgdQlu8+yvA283xtRx9+W5wM/DH3Eh8N3Ej/w28KfGmHncXqPvAl6dCL56vQoXKI7c\nM6ayoIhMSwfwj+P7rgOeFn6axBjzOmPMucaYZwEXWmu/CPwBcM/wOcmfsx/4fvjnZwC1Lf7s+4Sb\nxYLr17jhOI5fpPCstW+IslbGmEvDTPOTcAHQF8LHX22MeYS11gK/BL6Fywh/zlr7LdzqwCcYY/4R\ntwH2C6y1AfAB4Evh46cD77XWrgFvA64N/3untfZAeDinAHckju1fcQHVPwLfAG6z1q72O05c2fHX\nwxLnNcaY1w17/dq4WUSmIgx8vg2cBpSstXvCkt511toPGmMej2s4vyR8/s24gOYm4B24DFUb16T+\nh7hZNu/DZbY84GPW2veEy7nfiVsldAz4Y+AXwJXhf38PLAEVa+1rjDHnAF+01t4r/LnXAa8JD/sK\nXHB2Dm659zOttdGsHRERQMGViMhIjDGXAG+x1l407LkiMttUFhQREREZI2WuRERERMZImSsRERGR\nMVJwJSIiIjJGCq5ERERExkjBlYiIiMgYKbgSERERGSMFVyIiIiJj9P8BSfZMBu5ZOBAAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(train_X, train_y)\n",
    "\n",
    "X = predict_df[in_ts_names].values\n",
    "predict_df[\"prediction\"] = lin_reg.predict(X)\n",
    "\n",
    "predict_df.plot(x=\"timestamp\", y=[out_ts_name, \"prediction\"], figsize=(10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bYbcseRys6mo",
    "colab_type": "text"
   },
   "source": [
    "### Results\n",
    "\n",
    "* As seen in the above image, the Linear Regression model did not do a great job in predicting the pressure output.\n",
    "\n",
    "* It is normal for the first model in a data science project to not perform as expected, there is a lot we can do to improve the results!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3uvSIhCDK4iG",
    "colab_type": "text"
   },
   "source": [
    "## Random Forest Ensemble\n",
    "* Try an alternative model to predict the pressure output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YJKUXYc-K8bK",
    "colab_type": "text"
   },
   "source": [
    "### Once again, utilize *sklearn* to create our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Pw9ZHnFMLQKg",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 660.0
    },
    "outputId": "185f0762-ef42-4eeb-d4c8-bcb2257badfc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  series.name = label\n"
     ]
    },
    {
     "data": {
      "image/png": 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H4ISRAGOHnavzVSWuaHgzcVRev7GbtGE4561M9dqzVBaU3ILnHHauAODhMbmr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kmOea1cLTXuNm8j5CGe9PdLCREFhjlXQLESbHKmFXDZbwUkugUHyeHNw6wrOpHLGDUQl82MIPV8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\nnufh7W9/C5aXl0MlaBAajQZe//pX47d/+4VhCwwA3vKWt+PBBw/j1a++B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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnd_forest_reg = RandomForestRegressor(n_estimators=10, min_samples_split=20, max_depth=5)\n",
    "rnd_forest_reg.fit(train_X, train_y)\n",
    "\n",
    "X = predict_df[in_ts_names].values\n",
    "predict_df[\"prediction_rnd_forest\"] = rnd_forest_reg.predict(X)\n",
    "\n",
    "predict_df.plot(x=\"timestamp\", y=[out_ts_name, \"prediction_rnd_forest\"], figsize=(10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d21kj8CWNpNN",
    "colab_type": "text"
   },
   "source": [
    "## Possible next steps???\n",
    "\n",
    "1. Use a different model in an attempt to get better results in the pressure prediction.\n",
    "\n",
    "2. Using a different asset from the [Op Int ](https://opint.cogniteapp.com/publicdata/infographics/-LOHKEJPLvt0eRIZu8mE) dashboard, gather time series similar to it was done here, and create plots to visualize the data.\n",
    "\n",
    "3. Find the asset which has the greatest/least variance in sensor readings.\n",
    "\n",
    "4. Any other cool findings, creativity is encouraged!"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Introduction_to_Data_Visualization_CDP.ipynb",
   "version": "0.3.2",
   "provenance": [],
   "include_colab_link": true
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
